{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 丰田卡罗拉价格回归分析案例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "source": [
    "\n",
    "参考：[**Tolgahan Cepel**](https://www.kaggle.com/tolgahancepel)\n",
    "\n",
    "## 目录\n",
    "\n",
    "1. [概述](#1)\n",
    "1. [导入库并导入数据集](#2)\n",
    "1. [数据处理和可视化](#3) \n",
    "1. [回归模型](#4) \n",
    "    * [线性回归](#5) \n",
    "    * [二阶多项式回归](#6)\n",
    "    * [岭回归](#7)\n",
    "    * [套索回归](#8)\n",
    "1. [衡量误差](#12)\n",
    "    * [模型性能可视化](#13)\n",
    "1. [结论](#14)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  1. 概述 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据列:\n",
    "- <b> Age: </b> 车龄\n",
    "- <b> KM: </b> 累计里程\n",
    "- <b> FuelType: </b> 燃油类型 (Petrol, Diesel, CNG)\n",
    "- <b> HP: </b>功率\n",
    "- <b> MetColor: </b> 是否金属漆 (Yes=1, No=0)\n",
    "- <b> Automatic: </b> 是否自动挡( (Yes=1, No=0)\n",
    "- <b> CC: </b> 排量\n",
    "- <b> Doors: </b> 车门数量\n",
    "- <b> Weight: </b> 整车重量\n",
    "- <b> Price: </b> 售价（欧元）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  2. 导入库并导入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from collections import Counter\n",
    "#from IPython.core.display import display, HTML\n",
    "from IPython.display import display, HTML\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import warnings\n",
    "#warnings.filterwarnings(\"ignore\")#忽略警告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>KM</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>HP</th>\n",
       "      <th>MetColor</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>CC</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13500</td>\n",
       "      <td>23</td>\n",
       "      <td>46986</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13750</td>\n",
       "      <td>23</td>\n",
       "      <td>72937</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13950</td>\n",
       "      <td>24</td>\n",
       "      <td>41711</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14950</td>\n",
       "      <td>26</td>\n",
       "      <td>48000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13750</td>\n",
       "      <td>30</td>\n",
       "      <td>38500</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Age     KM FuelType  HP  MetColor  Automatic    CC  Doors  Weight\n",
       "0  13500   23  46986   Diesel  90         1          0  2000      3    1165\n",
       "1  13750   23  72937   Diesel  90         1          0  2000      3    1165\n",
       "2  13950   24  41711   Diesel  90         1          0  2000      3    1165\n",
       "3  14950   26  48000   Diesel  90         0          0  2000      3    1165\n",
       "4  13750   30  38500   Diesel  90         0          0  2000      3    1170"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = pd.read_csv('ToyotaCorolla.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price        1436\n",
       "Age          1436\n",
       "KM           1436\n",
       "FuelType     1436\n",
       "HP           1436\n",
       "MetColor     1436\n",
       "Automatic    1436\n",
       "CC           1436\n",
       "Doors        1436\n",
       "Weight       1436\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1436, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>KM</th>\n",
       "      <th>HP</th>\n",
       "      <th>MetColor</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>CC</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10730.824513</td>\n",
       "      <td>55.947075</td>\n",
       "      <td>68533.259749</td>\n",
       "      <td>101.502089</td>\n",
       "      <td>0.674791</td>\n",
       "      <td>0.055710</td>\n",
       "      <td>1566.827994</td>\n",
       "      <td>4.033426</td>\n",
       "      <td>1072.45961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3626.964585</td>\n",
       "      <td>18.599988</td>\n",
       "      <td>37506.448872</td>\n",
       "      <td>14.981080</td>\n",
       "      <td>0.468616</td>\n",
       "      <td>0.229441</td>\n",
       "      <td>187.182436</td>\n",
       "      <td>0.952677</td>\n",
       "      <td>52.64112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4350.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8450.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1040.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9900.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>63389.500000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1070.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11950.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>87020.750000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1085.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32500.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>243000.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1615.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price          Age             KM           HP     MetColor  \\\n",
       "count   1436.000000  1436.000000    1436.000000  1436.000000  1436.000000   \n",
       "mean   10730.824513    55.947075   68533.259749   101.502089     0.674791   \n",
       "std     3626.964585    18.599988   37506.448872    14.981080     0.468616   \n",
       "min     4350.000000     1.000000       1.000000    69.000000     0.000000   \n",
       "25%     8450.000000    44.000000   43000.000000    90.000000     0.000000   \n",
       "50%     9900.000000    61.000000   63389.500000   110.000000     1.000000   \n",
       "75%    11950.000000    70.000000   87020.750000   110.000000     1.000000   \n",
       "max    32500.000000    80.000000  243000.000000   192.000000     1.000000   \n",
       "\n",
       "         Automatic           CC        Doors      Weight  \n",
       "count  1436.000000  1436.000000  1436.000000  1436.00000  \n",
       "mean      0.055710  1566.827994     4.033426  1072.45961  \n",
       "std       0.229441   187.182436     0.952677    52.64112  \n",
       "min       0.000000  1300.000000     2.000000  1000.00000  \n",
       "25%       0.000000  1400.000000     3.000000  1040.00000  \n",
       "50%       0.000000  1600.000000     4.000000  1070.00000  \n",
       "75%       0.000000  1600.000000     5.000000  1085.00000  \n",
       "max       1.000000  2000.000000     5.000000  1615.00000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  3.数据处理和可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price        0\n",
       "Age          0\n",
       "KM           0\n",
       "FuelType     0\n",
       "HP           0\n",
       "MetColor     0\n",
       "Automatic    0\n",
       "CC           0\n",
       "Doors        0\n",
       "Weight       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Diesel'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m corr \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mcorr()\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#Plot figsize\u001b[39;00m\n\u001b[0;32m      3\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:10054\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[1;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[0;32m  10052\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m  10053\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m> 10054\u001b[0m mat \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy(dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m, na_value\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mnan, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m  10056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  10057\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:1838\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1837\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[1;32m-> 1838\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mas_array(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[0;32m   1840\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(result, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1732\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1730\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1732\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interleave(dtype\u001b[38;5;241m=\u001b[39mdtype, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[0;32m   1736\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1794\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m   1792\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1793\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[1;32m-> 1794\u001b[0m     result[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m arr\n\u001b[0;32m   1795\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1797\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Diesel'"
     ]
    }
   ],
   "source": [
    "corr = dataset.corr()\n",
    "#Plot figsize\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "#Generate Heat Map, allow annotations and place floats in map\n",
    "sns.heatmap(corr, cmap='magma', annot=True, fmt=\".2f\")\n",
    "#Apply xticks\n",
    "plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "#Apply yticks\n",
    "plt.yticks(range(len(corr.columns)), corr.columns)\n",
    "#show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Diesel'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m corr \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mcorr()\n\u001b[0;32m      2\u001b[0m corr\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:10054\u001b[0m, in \u001b[0;36mDataFrame.corr\u001b[1;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[0;32m  10052\u001b[0m cols \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m  10053\u001b[0m idx \u001b[38;5;241m=\u001b[39m cols\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m> 10054\u001b[0m mat \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mto_numpy(dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m, na_value\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mnan, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m  10056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpearson\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m  10057\u001b[0m     correl \u001b[38;5;241m=\u001b[39m libalgos\u001b[38;5;241m.\u001b[39mnancorr(mat, minp\u001b[38;5;241m=\u001b[39mmin_periods)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:1838\u001b[0m, in \u001b[0;36mDataFrame.to_numpy\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1836\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1837\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype(dtype)\n\u001b[1;32m-> 1838\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mas_array(dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m dtype:\n\u001b[0;32m   1840\u001b[0m     result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(result, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1732\u001b[0m, in \u001b[0;36mBlockManager.as_array\u001b[1;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[0;32m   1730\u001b[0m         arr\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39mwriteable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1731\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1732\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interleave(dtype\u001b[38;5;241m=\u001b[39mdtype, na_value\u001b[38;5;241m=\u001b[39mna_value)\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;66;03m# The underlying data was copied within _interleave, so no need\u001b[39;00m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;66;03m# to further copy if copy=True or setting na_value\u001b[39;00m\n\u001b[0;32m   1736\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1794\u001b[0m, in \u001b[0;36mBlockManager._interleave\u001b[1;34m(self, dtype, na_value)\u001b[0m\n\u001b[0;32m   1792\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1793\u001b[0m         arr \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mget_values(dtype)\n\u001b[1;32m-> 1794\u001b[0m     result[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m arr\n\u001b[0;32m   1795\u001b[0m     itemmask[rl\u001b[38;5;241m.\u001b[39mindexer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1797\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m itemmask\u001b[38;5;241m.\u001b[39mall():\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Diesel'"
     ]
    }
   ],
   "source": [
    "corr = dataset.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hu0t1/OZh0usslzle1IOIbz6yFcNQBzS6UAghhDheGIbiqnOX0j9aa/UaSTkmNS9kV6FGZ8bhynOXSONVIYQQ4nWaMxkjv/jFL/j5z3/On/7pn+I4DqtWreLmm2/mn//5n0mlUtTr9Qn3r9frZDLTn7B84AMf4Omnn279efjhh4/Et9CybbjCpoEyKceiHkSct7yTPzlvGYvye3uPRBq2DFXYU3LpyCRI2dPXH89l24YrWIYin7SwDDXr0YVCCCHE8WLNkjwffvsqzlyaZ6zmsW2owljNY+3Sdj789lVyGCCEEEIcAnMmY2TXrl14njfha5ZlYds2q1ev5tFHH51wW29vL6tWrZr2uRzHwXGcw3at+9NqtpY3CSJNsRaQdAyuOGsRr+wu8UjvcCt7xAs1A0WXtqRF2nEYrXqcKBUlXhDy8q4SYaQxDUUuaVL1AhldKIQQYk5ZsyTPGYva2DZcoVQPyCUtls/LyDonhBBCHCJzJmPkzW9+M4ODg/zDP/wDYRjS19fHV7/6VS6//HIuvfRShoaGuOeee/B9n8cff5wHHniAq6666mhf9rSma7ZW9yPKbsjCfJLTF+Vom9RorVgPGKv5dGYc0jN0r59rql6EY8VN5xzLYLTqM1L2eHb7KFuGytJ/RAghxJxhGIoV87OctaydFfOzEhQRQgghDqE5kzHS09PD1772Ne666y6++c1vksvluOKKK7j55ptxHIe7776bO+64g3Xr1tHZ2cktt9zChRdeeLQve1rNZmsv7CjQk8iiVLz5iTQMlz12jdVY2p5gtGaxp1hvZYiEkWao7JF2TDozDmNzPHskn7KwzTi2Z5uKfMpmrOqxfaTKXb/YRKHuS/8RIYQQc0YU6QPOGjmYxwghhBAnmjkTGAF405vexJve9KZpbzvzzDP5zne+c4Sv6ODsq9nalqF4czNW8cmmbJZ1pBit+RRre0fzVr2QmhfSnrZx/ZCqP/fG2poKTGNyZoxCKUWh6vPKniKnLcyTysfv2ws7CvSP1qQeWwghxHFpQ3+B+5/ZQe9Aedqg/3QBkJd2Fff5GCGEEELE5lRg5Hgw25ObZrO15oZmTzHCDyO8IEIpRYSmUPMpuwHzMg65dpvBUh2vMZVGA6NVn4RlMC9jM1YLWn1J5oJQQxiFjK8G0zqi7MYBouWdabKNcqNs0qInkaV3oCz9R4QQQhx3NvTHzcVHKl58WDIp6H/Z2kU81zc2IQDSkbYZKLkEoZ72MXJQIIQQQuwlgZEjaH+nPZONb7ZWqPl8+7HX8MOIQs3DaySIhJFmoOSStA3m5xK4QcRw2aMZAnGDCDeIaEtZhKGmMq5vybHKMVUrwLMvI1WfbtPANAzCSFOq+0SRJpMwSTgT/2krpViUT7FpoMS24Qor5mcP1+ULIYQQh0wUae5/ZgcjFY+e7r3ltc2g//M7xvifD77K/KzD4vY0qbxJ1Q14fMsIXhBxwSmdclAghBBC7Mecab56rGue9rywo0B7ymF5V4b2lLPfEbPNZmv5lM1A2cU2FXVfY07ax9T9iP6xOn6oWdKRmtKAtVgLcIOIjrSNeYxuggzAUOyzL4pi7z/aSGuqXki5HuAFEbmEhWkYzM8lySWmxvxSjonrR5TqwZTbhBBCiGPRtuFKq6y2GRRpUsTls2NVjwX5JNmkhWkoNPF6qhRsG66C3ruwTj4oEEIIIYQERo6Iyac9zY1LNmnR051lpOLx/Wf69zk5pTnCd7TiodGoGWIbhZrPrkKdbMJiYVtiQhAkiDSjVZ+kbZA9BifXRMR7t0jP/D44piLZvHYNJ3dmOGtZntMXtZFLWqQdk0X5JNO9QTUvJGHHU2yEEEKI40Fz/U9Ns26X3ICaF2IZBsG4TEs/jAgjyCYsCjWfkjvxQEAOCoQQQoiJJDByBOzztGc/JzdRpNkyWKZ/rErVCyi5AZZhsK9+qs3ymkItYHlnaspo34obUvVD8kl7SubJ0aaBhDX1n6Vq/Al1/J5YCrJJG1ScDRNEEeefMo83rZzHSMVjqFSnWPPRjSCL1ppdhRqrunMsn5c5ot+TEEIIcbBySYuEbVCbphTWDyL8UGNbqjWlDcA2jdbBSBhp/GDipkEOCoQQQoiJZEU8AlqnPfnpszRSjsme4tSTm8k9SXYX6xSqPvY0gYPp1PyQzUNV2tM2i/JJhsoufuNEKdJQqMfZIymlKB/F3iMGYBrxNSUtg3OXd/DU1hHqgUYRT6ChEcCJtKYeaJKW4vK1i3jPRSdTcUNySYuKG/CNR7ayq1Bn23CVpG3QkXZYlE9R9QI6Mw5XnrtE6qmFEEIcN5bPy9DTneWFHQV6EtkJByyWqfCjiLyTmBDkyCUt2lIWQyUX2zQm7BuaBwVrl7bLQYEQQgjRIBkjR8C+Tntg+pOb6XqSLJ+XQRP3EzmQTI+xqs9gyaUjbdOZcSbcVvcjKl4cWJhlvOWQi4C0Y9GRcTBMg9GKzxmL8ziNbzI+54prppvVRid1prnm95bR053jrGXtVL2Qv/u3XnaO1XjD4jYW5hMA7CrUeXFngSXtKenAL4QQ4rhjGIqrzl1KZ8ahd6BMuR5PmSvXA/YU47U9ZU9cwJVSnDIv01ozlab1mN6BshwUCCGEEJNIxsgRsK/TnulObmbqQL+qO0vfSJWBkssshrZMEESagZLX6MGRYKzqU2vU42jirBbbVGQSBhX36GSP+GGEbRpEGnq6cyRtkxf6C1TcvaOGTRV31f+vv9/TCnJM934taU9RcgM8P2RnsU5nxuGMRW1H5fsSQggh9iWKNNuGK5TqAbmkxfJ5mQlBizVL8nz47ataWaR7ivFku7VL2zlrWZ4fP7+rVbKbcuKxvMMVj9MW5uhuSzJa9dhTcluPufLcJXJQIIQQQowjgZEjoHna0z9am7Jx2VWoTTm5mbEniVKsXpBjtOrhh5oDjI0Acff6mh/SnrLJJm2Gy27rRMkPNX4YknFM3CAk2Ecfk0PJUFDxApRSLGizSdqKwZJLNmHxlp4uyq5PoRZgGop80iKI4Oxl7a3HT/t+KUUuaUPSJmFb9A6WZUyvEEKIY87kstmEbdDTneWqc5dOCF6sWZLnjEVt0wZQVs7PThs0ufLcJZy2IMejm4cYLLnMzyW4eGUX1tFKERVCCCGOURIYOUL2ddoz+eRmXz1JlnSkWDCcpH+0dlCBEYgnv4xWfRzTYH4uQdUNJ3Ssr3ghpqFIOwbVI9B7pBmY0VozVKpTcQO8oEzaMTENg7aUxSldWTrSNr0D5Sl10Qfbw0UIIYQ4mpplsyMVLz40yceHJi/sKNA/WptSAmoYatoA/0xBk5d2Fbnjpy9PCLr8+6uDU4Iuk+0vg0UIIYSYayQwcgTt67RnvPE9SbKTOsYrpTilK8tgqU49ONjQSMwLI/YUXXIJi+5cgpGKR9CIUoSRpuqFJG2DMNT4+xglfCjE74EmiDRVN4hH9ipIW5qBYsBQ2aMjbbOiKzOlLnpf7xdI930hhBBH1mwCCzOVzWaTFj2JLL0DZb7/TD9nLGqbVVBictDkQIMu4x83mwwWIYQQYi6RT4pH2EynPePtrydJxfXJJWzqgXdIrqnkBlS8gI60TajjZq1NdT9CKUjbBtV9zQh+nYIwnkCjAduGMFTUvJBas9+JAtcPOWPx1D4hB9rDRQghhDhcZhtYmLFslvgQZFE+xaaB0kGVgR5s0OVggylCCCHE8U4CI8eg/fUkSdombWmb4YrHoQpVRBqGKz4Jy6A7l6BQ83EbTUa0hqof4ZgGGt0a+XuoNZ+15msMmPi9aaj7IS/3F/nSLzfx4bf1kLANHu0dpuYHLJ+Xpm+4yoadBTpSNpmEhRuE7C64dGYd/vicxa3Nn6QICyGEOBwOJLBwOMtAtw1X2LSnRC5pM1LxsM04a1IpNWPQ5VBnsAghhBDHEwmMHKP21ZNkzZI2/umJ1+gzFd4hDlK4QcRAySWXNMkkHEarHrrxEl4YhyqStkH9MGaPANMGfEINvUMVto1UWb99NJ46E0ag42CSYxrYpsHWsIIfRqAU2YRJ0jb5/rP9rU2epAgLIYQ41A40sHA4y0DX942xaU8ZFEQRmIZq9evqzDjTBl0OZwaLEEKII2Pjxo188Ytf5MUXX8S2bS6++GI+8YlP0NnZyfr167n99tvp7e2lo6ODG2+8kWuuuab12B/84Ad85StfYXBwkBUrVnDrrbdyzjnnABCGIX/913/ND3/4Q2q1GhdeeCGf/exn6e7uBmB4eJhbb72VJ598EtM0ueKKK/j4xz+OZcVr2P5e+1ggbcmPYWuW5Ln1sjP47BVv4FOXnc5nr3gDt1x2Omcva0ejsMyJP75DeX5TqocUaj6daYeMM/E0q+5HWIbCNo/OiVEQaQbKHnU/wlQK04gn6lS8kFLdx1DgWAYGGtcPKbs+67ePcvuPXuL2H7/ECzsKtKcclndlaE85vLAjPuHb0F84qOuJIs2WwTLr+8bYMlgmOsz9WIQQQhx7DiSwAHvLQHcVamg9cd1oloGu6s7Nqgx0/Dr08w27uO/pHVT9EFPFARjHMhipeGzoLzBS8aYNurQyWJyZM1hcPw6myLonhBDHnnq9zvXXX88555zDb37zG370ox8xNjbGX/zFX1AoFLjhhht417vexVNPPcUdd9zB5z//eZ5//nkAnnjiCW677Ta+8IUv8NRTT3HFFVdw4403UqvVAPjqV7/Ko48+yv33388jjzxCMpnklltuab32Rz7yEdLpNI888gj33Xcfjz32GPfccw/Afl/7WCEZI8e46XqSLJ+XIe0Y1P2JE2MO9bYkjDTDFY+UbTIv6zBW9Qkbm59mk9aEZbRKbo40DVMyZkINJTds9SsBzaaBCrYBjmWSTVj8wanzUUYcVHq9KcLSpE4IIQQceGnM/spmOzPOlGbj0xm/DtW9kP5CjSCM6EjZVP2QlGNimwZtSZti3WfrUJlcwuKsZR0Tgi6zzWDZXajxf5/rl3VPCCGOMTt37uS0007j5ptvxjRNHMfh2muv5WMf+xgPPvgg7e3tXHfddQBcdNFFXH755dx7772sXbuW733ve1x22WWcd955ALzvfe/ju9/9Lj/5yU+46qqr+N73vsdHP/pRFi1aBMCnPvUp3vzmN9PX10cURTz55JP8+te/JpVKsWzZMm666Sb+6q/+iuuvv36/r32skIyR49BLu4oMllyieHALlgHj900KcMw4k8IyJn79YNT8kOGyRzZh0jZps+QGEYbiqGWPzGRykMiPaGWUlCeNIJ7uJG82mrXkhzoDRQghxPFnfGBhOtNlaTTLZs9cmmes5rFtqMJYzWPt0vZZNTqdvA515RJ4QYQfaupBhKEUhZofl5cCjmmwu1AnaZtTgi6zyWDpTDvc90y/rHtCCHGElcvlCX88b+oQjhUrVvDNb34T09wboP/5z3/OG97wBjZt2sTq1asn3L+np4eNGzcC0NvbO+PtpVKJ3bt3T7i9q6uLfD7PK6+8wqZNm2hvb2fBggWt21euXMnOnTspFov7fe1jhWSMHGeaNczoOPgRRLoVIGlmSRgq/rDvGIqergzbR2sU6sHrzigp1AIsQ9GZsSnXw1bPkUhDFGpsUxGE+pBnrhwsQ8H4vZ0mbiJb9wNySXvCfQ+0yZ00qRNCCDHewU5IW7MkzxmL2mbVFLzZPLxQ8ynUPO773Q52jtV4w+I2DMNguOyiUHSkLYr1gIwTB2uKtYBapDEUpByLq86bmt2xvwyWjrSNBkZl3RNCiCPukksumfD3D37wg3zoQx+a8f5aa+666y5+9atf8e1vf5t//Md/JJVKTbhPMpmkWq0CUKlUZry9UokPjtPp9JTbm7dNfmzz783H7+u1jxUSGDnOtGqY21MMlj0qXkAUAUoTBfGmJyIOkqQdi5PnZzljSZ4XdxbZPFim7kezClzYRhxUCDTjylLiEpqRik/KNsg7FsXa3oCL3xi5a5vqsE2uORDjS55NFZfZAOwp1AkiqHshScdkYS5BzYsOqMmdNKkTQggx3uspjZmubHayZsnMc9vH2DlWo+aH1P2QdMLCCyNO6cpimwamoQiieA/gBhFrlrShlMIPIrwwIgg1Zy9rn/Y19tX4/YJTOvn2E6/JuieEEEfBww8/TDa793er4zgz3rdcLvPJT36SF198kW9/+9uceuqppFIpSqXShPvV63UymThYn0qlqNfrU27v6OhoBTWa/UYmP15rPeW25t8zmcx+X/tYIYGR40yzhnnRvDTzsg5hMQ50xFNidCtzxDINurIJcgkLlOKMxXkiral6AduGa/t+EeLSk6bpQhw1P6IeRLSlbLwgotbod6KJAySWoYi05ljpxzY+TvPqnjJ6T4VIawylSDsGHRmHPzh1waya3MHhHbMohBDi+LSvwMKV5y456B4czZKZHaNVhis+odY4lkHFDaj7IQNFl4obsmZxG20pi5GKRy5hEUYaP4iwLZNIawZLLm9c3rnPtW6mDJYX+guy7gkhxFGSzWYnBEZmsn37dv7sz/6MxYsXc99999HZ2QnA6tWrefTRRyfct7e3l1WrVgGwatUqNm3aNOX2t771reTzeRYsWDCh3GZwcJCxsTFWr15NFEWMjY0xNDREV1cXAJs3b2bhwoXkcrn9vvaxQnqMHGdaNcx+xIquLNmEjWUo2pJW3FfEVCgFmYTJKV0ZaJzq1LyQedkEf3r+yTiHqB+I1lCo+QRhRHvKntDnpFnic6he61ByQ41hxI1jDQOK9YD+0Rrzsvas038PppZcCCHE3DfTRLmDDYo0SzeHyy5BqAmjiLakTdqJp81oDZGOcIOQbcMVls/LkLDMRm+RkFcGyjyxdZjHtoywq1BnuOLx0q7ihOefPGGmmcFy1rJ2VszPThkvPB1Z94QQ4ugqFAq8973v5dxzz+Vb3/pWKygCcOmllzI0NMQ999yD7/s8/vjjPPDAA1x11VUAXH311TzwwAM8/vjj+L7PPffcw/DwMJdeeikAV155JV/96lfp6+ujXC5z5513cv7553PSSSexfPlyzjvvPO68807K5TJ9fX185Stf4eqrr57Vax8rZPU6zkyoYe7OsmZJni1DZQpVP06VDSPakhZnL2unIxOnWDVrm89ckqd/rEomYRHVfIJ9ZHOYKo6pzGbgjB9pxmo+WcdEK6i4ezdNXqgxG2ks4dEZXjNFxlZEKMJIo5QimzDxQ81Pnt/NB96yEsvaf7zwYGvJhRBCzH2zKY2ZrWbpZlvKoX+sTtqxUEphm/Fo+pofN1vNJgwKtQDbNFizuI0nt41Q9yOCskfSNliUT7Ion2TnWI11v9zEh98en9TNdrKarHtCCHFs+/73v8/OnTv56U9/ys9+9rMJtz377LPcfffd3HHHHaxbt47Ozk5uueUWLrzwQiCeFPPpT3+az3zmM+zZs4eenh6+8Y1v0N7eDsDNN99MEARcd911VCoVLrjgAu66667W869bt47Pfe5zvP3tb8cwDN71rndx0003AdDR0bHP1z5WKD259biYolwuc9555/H000/PKoXpcGum1I5UvLiG2TYYLLtsG4obsi3MJ1k5Pzeltvmqc5fw9Ue28tpwhZQdp9UWaz4VN2RyzMIy4s4iBzqJ11CQS9pUvWBKnxGr0Zz1aOtI2+SSdutUzDYVNS+k6od86dqzueTU7lk9z5Sfw6T3ezYTBYQQ4mAda2vTXHAsvqfr+8a448cv05ayWN9XIJe0WkEJNwgZqXh4QcS8jEOoNacvbKPqBQyUXBKWwYquDI5ttkprtdb0DpRZ0p6i6gWMVv1Zr2Gy7gkhxJF1LK5Lc5VkjByHZqph/oPTFnDWsjzP9Y1NW9scRrqVAmsZiiBSpGyLehChG41Tx8dBooPI8Iga5TWOGafdjq81DhqvYShFeBTjcX4YYZsGjCuTdiyDYj1gsOTO+LjmNIBmzfUZi9oOSy25EEII0dQsYQkjGo1V4ylwAAnLpC1pU6z7eGFEGGmqfsjyriyRhqUdabKTSluUUixqS/LUayN0ph3WLMnPesLM4eqhIoQQQhxtEhg5Tu1rvN/laxdP+/Utg2VSjkkQRgyWPcJIE2lNOM2I3TB6fWN3vVDjhQHZhIUfRriN1BMNhFpjGnEpy9Fgqql9RLwgwjIV83OJaR/TnAYwXbrxrZedMasxi0IIIcSBapawPN83RlvSYqTq0Za0G8EMTRBFLGlPkrBMVnRl+PAfrqJUD/j8TzaScqZvlBpoTbkesLIre8ATZg5kvLAQQghxvJDAyHFsphrmmb6+fF6GxfkkL/QXiCJNwjLi6TFRnB0yPkxxqEIWZTfAVJBLWPFo4cYTN4Mi48foHim2AfF3GG/itI4o1nxOnpfh4pVdU+4/JXU4H6cOv7CjQP9oTVKHhRBCHDbjxwDX/BDTMCjWfRzTwA1CbMPANg0Wt6e44ZKV9HTn2DJYbjVKnZwxAlCu+wDT3gb7nzBzKHuoCCGEEMcCmUpzglFK4ZgGir2TYyzz8P4zCDWU3ADHNEjZ5pTbjvQh01A1YHehTs0LqHkBA0WXlGNywyUrpjRebU4DGKl49HRnySYtTEPF6cbdWUYqHt9/pp/oWJlLLIQQYk6JIk3aMfmPaxZy2qI2FrUlMJWi6odYpsmi9hQXruiaEKRvZpnsKtSY3EpOa81ozY/Xs0bPkWLNZ7jsUqz5aK1lwowQQogTjqx4J5BtwxVGqx7nntTOtuEqA6V6q8QlaRloNG4Q9wE5HB/z643Xyjgm9SBqZY00YwpHKnvEVFAPQuqliLRtcvK8DDdcsoLL1izi4VcGGCy5zM8luOiUeTy2dZhnt4/RkbaZHL/ZX7qxEEII8XpMKeO0DFbOz/Kei06mK5ugLWWTT9lTSlnGZ5n0DpSnNEpdnE+xoitD70CZINQU6wFhFJe5tiUtLFNx4YoumTAjhBDihDGnAiNjY2PceeedPPzww0RRxBvf+EY+85nP0N3dzfr167n99tvp7e2lo6ODG2+8kWuuueZoX/IRVaoHuH5Ee8omaRtkExZeEGEoRXvaZn42wYu7iqzsypJNGJTckG1DZap+xKFMiKh4IZYBacek6u0d7RvqeEQw+vAEZprmZWxOX5znteEqqxdk+fK7z+WnL+3mir//DTsL9VaTWNuMM0NGKj4Zx6R/rMaKrmxrDDLsP91YCCGEOBgzlXG+NlKl9GKw3zLO/TVK3TxY5rHNw9S8kLaUTSph4gYRO8dqpByTs5blZ903ZHJz8pl6jsz2fkIIIcSRNqcCIx/60IfI5/P84he/wDAMPvnJT3Lrrbfyl3/5l9xwww18+MMf5tprr+Wpp57i5ptv5tRTT2Xt2rVH+7Jft9luNHLJuBHq8zvGCLTGMQ0SloHWcS+QkhtgKkVXLsGCnMOuYp1dYyZV7yDG0+xHEEHghaRskyCKWqN9mxm/h7M5az7tsLg9TVvSYazm8b8f38bXf72ltTlUNgxVXCqepuQGpGwT01CMVDyqboE1S/Kt4IikGwshhDjUJpdxznZqzGQzNUoFuP+ZHczPJfDDiFI9pOyGmIZicXsKy1Ss7ytw+drF+w1c7Ks5+fjAzWzvdyRIgEYIIcRkc+bT3IYNG1i/fj2//e1vWzOeb7vtNgYHB3nwwQdpb2/nuuuuA+Ciiy7i8ssv59577z3uAyMHstE4qSONG4QU6j6Woai4IVprlFJYRtxzJOWYbB0s8uz2gKoX4k8zseZQqvkhijh7pOaFrdcKozhrQykOabYKwNJ8EoizPXYXQ/7pie3UvJDutgRKKYbKLgpF2okb19WDEMcyaEtaFOsBW4cqdKRtNLCrUGPt0nZJNxZCCHHIbBuutEpgZjs1ZqYP+9M1St0yWKZ3oMzK+TmyCZOSG+A3prMBFGs+z+8YY8tQmZ7u3IzXOdvm5MdSE/NjKUAjhBDi2DFnAiPPP/88PT09/Mu//Av//M//TK1W4y1veQsf//jH2bRpE6tXr55w/56eHu67775pn8vzPDzPa/29XC4f1ms/WAe60dg+Wm2M6IWaH2Gbe0f9uUGcpaGAHaN1Qg2WcXhLWpo0UPVCHFOhlJow2lc3mrMeyuBI2Y+fv+aF1LyQoYpHW8oGFFUvpO5FrfciYZt4QYTWUKzHDWTHqh67i3VK9YDOjMOV5y6RkyYhhBCHTLP0NZWfftzu5DLOA/2wP+H5lSKXtBmteGwaKFOsBQRhhBdG3PXQJv7rJSunfY7ZZrWctiB3SLJfDoVjKUAjhBDi2DJnAiOFQoFXXnmFNWvW8IMf/IB6vc7HPvYxPv7xj9PV1UUqlZpw/2QySbVanfa5vva1r/HlL3/5SFz2QTuYNNtCzWes5pO0DNwgou5HEwIfUagZrvgo4mBEcOgraPbJCzWgSdnx9TWDIYe6OWsUxpkyuwo1OtIOW4cqAAxXPOp+iBdGGBqMSGE13rtF+SReGFGo+VS8kNGqz7kndXDluUtkEyWEEOKQyiWtacftaq0p1QOKNZ9QazIJs/Vhf7js0pZySKQMwgie7xujf7TGh97WQ8oxeXVPCVCsXpAlkzAnPP9oxWNDf4F6EJJ2rPjgxIdtQxXW/XLTtAGD2Wa1PLp56ICzXw6HQ1WeJIQQYm6aM4ERx4l7PnzqU58ikUiQzWb5yEc+wp/8yZ9w5ZVXUq/XJ9y/Xq+TyUxf/vCBD3yA97///a2/l8tlLrnkksN38QfhYNJsizUfN4hQCrwwjnoYingKjYZmHMRQcWmLG0SNYAWHbVLNdGp+hKHiSTn1cdGZ8BBlj2wbrqEMk86Mw5t75vHsjjFGKnGGkGmoVgPYUGvCSGMoxbxsgkX5JHuKLqNVl//2h6t4y6r5snkSQghxyDXH7b6wo0CPk6HshQyXXHYX69T8uB9IW8ri24+9xkjVY8dolSDU9I/VJ0yXGal6/P++8xw1P6Dixs3Os0mL807qoCPtsHOsRo+TYctQmXoQ0pa0Gz1HAjrSDmcsyrFlqDptwGC2WS2DJfeAsl8Ol4PZNwkhhDhxzJnASE9PD1EU4fs+iUQCgCiKP1Sffvrp/NM//dOE+/f29rJq1appn8txnFag5Vh1oGm2AG0pG8dQDFd8tAbTiDcDWusJwYZIA0phmQZeGG+kjlRQZPw11IN4NGEYaYLJo31fR3PWuhe0uvKvnp/lroc2UQ0j0o6JoeIskWaPk1CDZcKCXPzvoVT3OfekTgmKCCGEOGya43Zf3lXk314ZxAtCql5IpMEwIJ+06Zmf5XfbR+kbrmKZBkpB2rGwDEUQaQZKLhUvIIo0mYRFPmUDUHEDHtk0xLKOFJmkxYZdRUYqHqZSDJbq1P0IpRSm4fPcjgIL26YPGEyb1aJ1q1+JF8Zr+PxcYtrsl6bZNDE/FM1SD2bfJIQQ4sRhHO0LOFTe9KY3sWzZMv7iL/6CSqXCyMgIf/u3f8sf/uEf8kd/9EcMDQ1xzz334Ps+jz/+OA888ABXXXXVUbnWKNJsGSyzvm+MLYNlomk+4O/vPuM3JNOZbqORT9nkUva4IIiKR+NOGo+roZUpcbQ/+7tBRBBpkvbEf6rjm7MeqM5sgsvWLiTtmGwfqzK/LYFlKOp+RN0PiRqBolDHmTJJy2Sw7NM7UCZhG5y5JO7wP93PTQghhDhkdFw+U2+Ul5oKDBX340o7FotyCapeSMULaEta2KaBUgrbVEQ6Igjj9SybMHGs+E972sEyFXtKLt3ZBMvnZah5IaNVLz6QsE3m5xwyCYuRikfvYImRsjclYNDMatlVqKG1ZrTi8fT2UX63bZSnXxvld6+NMlxxaU/bE+434dtrlLWu6s7N2MR8Q3+B2378Ep/+1xe548cv8+l/fZHbfvwSG/oLB/RWHsy+SQghxIljzvz2t22b//N//g9f+MIXeMc73oHrurztbW/jU5/6FG1tbdx9993ccccdrFu3js7OTm655RYuvPDCI36ds2mQNpv7TEizTWQnpIU2NxqTp6Usn5dhcT7J1qEKhmo2N42nzjQf3dyyRJHGsuLsicM9mWY26o1msQrVKgPSjf8wDQgPoB9K31idm779DIvbUyzpSGEZBgvaEvSN1qfcVxFP6+kfrWKZijAy+ecn+/j+s/3SxV4IIcRh0eyHEUSaNy7v4Mlto1hK4dgmlqEo1n22DlVYmE+Aiu8fRGA3kiH8UOMFe0thI713j9AMqlS9gP5CnZt+fwVPbh2m7kFb2sFpBFcA2pI2Y1WPUTwyiYmZFs2slv7RGs/vGGOk7OFHEQnLBAUZJ95i/v2vNnPZ2kX0j9ZapSypxiS6XYXaPpuYH8pmqQezbxJCCHHimDOBEYAFCxbwt3/7t9PeduaZZ/Kd73znCF/RRLNZ4IFZbQLGb0hmu9EwDMWlZyzg8S0jgMax4iyMSGvcRl8PQyn8SONHUdx81DTww+lPV440v9GcNdlozto8eGoGRWZbXmMS91sp1H22j1SouOGEXibjRUAQxu9NyjZZ3J5uvc/SxV4IIcThML4fhhuE8Qj5hNX6MJ92LAo1n66sg2ko/CAijCJss7GuR5qocfARr+0Tn99sfKHmhdT8CMs0ME1NwjLYe1QSZ2VOt6o2S1vCSHPlOUv40i9fpeqHJCyDSMO8jMPyriwdaZvegTLr+wp88G09/ODZfnoHyuwpxoc+zbLW1zP1ZrbNUg9m3ySEEOLEMacCI8ey2Szw9z+9A2DWm4A1S/J8+O2rWtkls9lo/On5J/O/fruNHSO1Rg+W+DXi0b1xiUraMXFMRdWPCMO4Was+2ikj49T9CFOB1Ziu0xSX/+y/OWsI1BqPq/v7TzXxQs1Q2eXsZe0oI950Shd7IYQQh8v4fhiRjpupBpFurdWWoag1SkqTtkkUaapeiGkYWIZqlYQqwDQMTGNqOSrEfTUMBR1pB6U9CjV/Qp+SqheQtuPym2bz1slZrZHWFGoBqxfk6Mw42GZcjtLcwzSbmmYTFrdedsase4UcjmapB7NvEkIIcWKQwMgRMpsF/vn+AgoOaBOwZkmeMxa1zXqjYVkGH/nD1dz+o5eouAHphNUa3ztS8VBKcdrCHCu7smwfqdI3WqXiBnSmLXYVvThTY/x1jfvfzZMpx1S4weEtvwk1hI3mrEEYtcb47h3tqwgPYTSn5kfsLtYbTWAV+ZRFW9I+4I1ZEEQ8unmIwZLL/FyCi1d2YVlzptWPEEKIQ2B8P4xc0qItFff7aEvaKBUHLUxD0ZaycEwDI2kxL+NQqofUGrdlEiZlN8RQulViA3HZSNULMJRi7ZJ8K6AxL+Owu1inWAtazzEvk2BBWwLduKbpMl93jVUp1gMo1JiXTdDWaPLaNL6pqWGoWQcxDlez1APdNwkhhDgxSGDkCJnNAl/zQlDx/57pPtNtAg5kowHwzrOXAPC1hzezs1BnrBZgmYqT52VY1pki0vDaSJWEZdCetknYxt6Gb0xt1GqbCk3crDVpmXEPEKWPyCibZsbI1NG+elwfldf/OjUv5PEtI61gi20azM8mWL0gh+vPbmP2w+f6W+95EGosU7E4n+QDl6xs/UyEEEKICf0wurOc0pWl4hYo1n3SjknFDWhLOgyUPFZ0ZUBBEGqWdlitg4HdxTq7C3X8UDNa9ckm4i1fxQ2INJy2MMtV5y1lRVe29VrnLmun7IX4QYRtGWQdk97BCmuXtnNSR5o7fvrylKzWfNoh45jUvZBtQ2U60h0TDncOtqnptFNvxnk9zVIPdN8khBBi7pPAyBEymwU+5Zioxv8+lJuA6cbcvfPsJVy2ZhG/2TzIS7uKpGyLi3vmsWJelu2jVUr1gLGqx9/+4lVGyh6B1vEpkAIvCONO+QAaFBqFoiPl4FgGZTcgaZt4fkTVPzL9SepB1Eox9sOJo32bKcGvR0QcbHEa9dtBpNlVqFN2A5Z1phmreqzvG5vx5OmHz/Vz+49eouaFtKVsEqk4S2f7cJXbf/QSgARHhBBCANP3wzhjcRu9e0oMVzwMQ9GetlslIECrPKTix3uFi1Z0cdayPL98eYDfvTZCoeYDcSnoG0/u5M/euqJVOtJ6rcEKi/Ip8mmHmhfSO1hp9d7YPlqdNvM1l7DIp20GSy6Fmk+pHrSyRl5PU1NpliqEEOJIksDIETKrBb6xQXmh/9BtAvY14Qbg4VeHWrf928aB1m1nLWvn2e2j7CrWCaKI9rQDxFNqSm6AF4RxAEJBVzZBoeYTao0farpzCZZ3Zdk+VOLlPZVD8wbOQjMgkrAMvHElP0GjDtt4neU1CcvAaPxMDEPh+SGFmk80XOEbv96CF+ppJwgFQcTXHt5MzQvpbkugVBxcSTkGSdtgoOjy9Ye3cNmaRVJWI4QQApjaD8P1I5Z0pDj35E4u7pnH2cvaJwTiZyoPuXztYrYMlXl1TwlQrF6QZUVXdkIAf7reG44VZ5Je3DOPtGNSqPnTZ74qxYquLJV6QKEeUKz5ZBLW625qKs1ShRBCHEkSGDlCZrPAX3VeHKzoHzs0m4B9TcF5eWexlXo70/SbYs3HDSLStkmzm0jCNnEsAz/U1LwAN4y48fdX8J2ndmAbinzaYWFbAsMw2Dp4uN7NfXODCMsAw4gDJBBnt4SNBnazmVwzmdF4ryyDVjPaSMdBl4obUPNDutuSmEpNmVbz6OYhdhbqtKXsVlCExlUFESQdk77RKr/ZPMjvn7rg0LwJQgghjnvNfhhxYKMMaFYvyE0JbMDM5SGGoejpztHTnZvVa20brrC+b4zfbBpioFjnO0/28YNn++nOJvDDaNqs1o6Mw8ruLL2DZap+yLahyiFpairNUoUQQhwpEhg5gma7wB+KTcC+puCsdDL86pVBFPAHp86fcdLKZWsXkjANvDAiqXXrOZRS2CbUFNiG4vEtI4xWPYq1gGyizq6CzSldWcyjeIgTREAUkbTi62/GQppBkQMtr7ENRcI240yUSDdGGDZLdjRbhir0j9VpS1mcMi/DcMVrTasZLLkEoSaR2hsUcYOQUt3HCzRRFBFEmm8+spWubFI2ekIIIVpe2lWcMfPzUK8XhqGoeiE/3bB776FK44DmtZEqwxUXNwhZu7R9SlZr1Qt5xxkLue7Ck6i44SFrano4mqVOV2IsmSdCCHFik8DIETabBf5QbAK2DVfYtKdELmkzUvEmjM8ruyFRo6Sk7IXkkns/sI+fflOud7G4PcXOQr3R8M1qZVxUvQCt40DDayNVeuZn2TwQnxQNlVzKbnBMzPitBxGGgqRtTBjNGzRG+8L+x/tCnHHSmbYJojgQ4gYhw2WvVaKTsAzQmsGSS8UNWDk/15pWMz+XwDIVbhCRcgzcIGS04sXjhQ0FKn7fdxfqrPvlplamiRBCiBPbhv4CX3roVXYV6nSkHeZlnWkzE6fT/PBfqPkUaz5tKZt8yt7nfmJfhyo9iSxVL6DshmzaU2Jxe3razNf9ZaYcjEPZLHVfJcay9gohxIlLAiNHwWwW+Ne7CVjfN8amPWVQEEW0xvqd0pVFNwMWGvxxk1yaUo7J7kJIyQ1Y0pGiWPcxMCm5IUEYNe5jUKwFJCyDNYvaUIZB2rHYMlSmUPUp1gJ0eGAj9A6XSEPdj7NHgki3MkUOpDmrF2qGG6MSE5bBWNUjHtwbB0qGym4rDlR1A0zDYEEu7r2yOJ+iI2Wzp1gnYSpKdR8/0qA1nhcRAZYCtGbHaLWVaSKnV0IIcXw6FBkJUaT5+q83s35HAUPBnqK7dy2flJnYfO7m6z7XN8ZveofYMlBmV6GOG8bj7RfnU5x9UvuMQYBtw5VpG6xCHMBfOT/HjtEqJ3dlGCy5x11py75KjPcXaBJCCDG3SWBkDtrQX+C+Z3ZQ9UNyCYtM0iSINCMVj4pb4JSuRvNWBfY0zT53F+r0F2rc8+g2an7IWNXHVNCVS1Cqx6dFwxUPP9BkkxajtYDOjENHxuG8dAclN6C/0b2e4MhMpZmNehChgJRtUhs3LSdolMYY7Ls5a6keUA8icgmzNSZY0yjbGScMNTtGqpgKvv3YawyUXQxD4UeavrEaNPqTNF/JUPG4w7F6gOlFPLt9lG3DFRklKIQQx6FDlZHwi5d282jvMFprskm7FcSP1/KQlfOzrczEFfOzrdd9bvsYW4cr+EFcRpqwFGnHwg1Cdo3VcINwxiBAqR5M32C1IeWY2KbBey48mXzKPq5KUfaXDdMsI5aDCSGEODFJYGSOaS78rh+xsC3BaNVvbWTakjbFus+eYg2D+PQn60zc/IyUXZ7fMUbCMljUliSVsJiXcdiws8CmgTKWYZBJmMzLxNkQbhCxob/AmiV5OjMOKIUfavY0+moYanalKkeKBmp+iGMp0HEmCMRVPyF6xuwR1XisF0SMBhHGfobHRMD2kSqdGYfF7el4/GHK5sWdxVZQxVBxtkp7yiGXstFaU6z77ByrtcYqnkik5lsIcbw7VBkJUaT50fO7cIOI+VkHo7Ho2KZqreW7CjXaG8GJ8a87WvUwFQRa4wURkVZYhkHSMqj6EX4YMTJNtglALmmRsI1pG6wC1Lx4FHA+Zc8YvD9Wf5fvLxumWUYsBxNCCHFiksDIHDN+4e/KJtjQX6BQi/uDWIbCMQ32FF1O6cqQckx6Byut5mpVN+DZvjEAzl7WTjZlA7CkPcWuQo26H9KVcThzabype/q1MWxTUfNDtg2V6Uh3ALB1qEzdC3Esg0BronDmyIhlQEfaZqzi4x/BAIoXxC+WduLskWaiSDMoYpuqNf4X4iBGotGnRAGmUgTs+4JDDfMzdmtzeerCNrIJk9/0DmMZio60Q9oxWxtepeKfT9UPKZ5ggRGp+RZCHO8OZUbCtuEKuwp1krZBqGF8LF6pOANktOrRlrLJJEzufWI7IxWPBW0JdozWMJSiFkRoDW6gcQMPA1r9rvIph00DJbYMlTGUagUxluZTdGcTvLSryEmdSXYWXCpuSCZhclp3ll2FGmuXxmOCp3Ms/y6fTTbMnmJEqX5slAELIYQ4siQwMseMX/izhsWaJXm2DJUp1gJqjYajKcfivW9azuoFuQnTb0KtsUzFaQvzdGYTe5/TDSjVQ7IJi4oXUvFCFuaStKUsRioeacekUAtam4liLe6hEYUaxd5si+lEjQauXTmHPSXviGeXVL0Qx1QYhprQnNUP49G+aE2o4yBHGGnsRkaJ3k9QpGnD7hJvyaVaf0/aFrapiCJwLKMVFInFTV0TlklbIyh1IpCabyHEXHAoMxJK9QBDKdrTNmNVn3zKJl5NY6YR985anI/Xl+brekFI3Y//jC/ZjC8iXsfCKOS14Qq5lM2XHtrEWM3HbWSSuEFIGGm2DlV4eXdpwjW90F9kWXuSK694w7SBnWP9d/lss2Fy09wmhBBi7pPf/nPM5IV/fN8PP4jwwogg1Jy9rJ0V87MTpt/0j1W559HXWJhPTXjOobJLoeahtSaI4IUdBXZma3RlE1TdkIoboLXCDUJcf+9pi20ZEOpGo9EZLlhDsRZgHMXZvl4j8pF2TFw/avUZaY72dSyjkY4cfy3SwCxbp4xWfPS4UceOZZBxLEr1gGLdJ59SrfKdqhdgGwaL8snGJnjuk5pvIcRccSgzEppr+eJ8iroXTcj8DCJNqe6TsAz+aO0iKm7Yet1IR3hBvI41l1017r+bTcMrbnyYkbZNTunK4lohz+8Yo+wGhFq3ykwn2zFW519+t501S86c8PXj4Xf58nkZerqzvLCjQE8iO2Xc8P6yYYQQQsxt++mUII43zYV/V6G2d/qMUuSSNh0Zh1I9YNWCXGvhb06/OWtZO6ctbGsFVZpGKx5bhypxiYlS2KbCsQxGKh7bh6ucNC9NLmkTodlTdKn6ccqtbRmkHTNuTLqP5ApNfJIWhfqo9yKpeiGGistrxvMaI3/jCTTx/2mcWYYUI60nbIJzCYu2lI1jGbSnbLwg3iR7QURn2qEz63DOSR0nzMbsQE5YhRDiWDb+YGI6B5KR0FzLq17IGxa30ZlxWuuF64c4psGbe7q49IwF5JIWjqUYLLmMVX0idByQn/ScSsW9teKm4ZpQa07qTJFNmGwbqRBoTVfGpuZPnVYXl5DGj/3uU33UJwV3joff5YahuOrcpXRmHHoHypTrAWGkKdcDegfKdGYcrjx3iQThhRDiBCUZI3NMc+HvH621NikpJ05n3VWo7XPhn3KaAmwZKhNGmlSjaVvaMUnZZqN8xmeo5NKVdTjv5A7+84Unk0ta3PWLTfz7q4P4RogXRPssOtGAbcK8bJLdxbhh69GMj/iRxvdC0o6JH0atPiNRo1GrYxpoHbHvAqG9bAP8cO8mUxMHXuZlE3RlHdpSNqaKp+GU6sEJtzGTmm8hxFxxKDMSxq/lwxWPU7tzBFpTrvuMNsbA/9lbV2AYioobMFLxWr1FgsZBQ3OFao2Wj3QrfUQTT2gLo7hctlgLsAyDXSV32utpHmIorXEDzRcf3Mh1F55MxQ3JJa24Gftx8Lt8zZI8H377qgllxMfTuGEhhBCHjwRG5qCDXfgnB1VySYtC1ccxDYJQYxlxj5IgivtvOKbBrmKd+W153n/xKa3nvfysxTyyaZCRij+rIEcQKYIobmiqzHjzFhwD2SOWEWd4lN29bVa9MEIpcIz9B0YUEBE3cQ0j3QpOLe1Ic9naRTzXN0bvQJmKH56wGzOp+RZCzBWv52BiOpPX8mYz0/OXz2utFS/sGOMLP9tIzY+wLYMwjDMcQRFNGj+vjMY6qxS2aZCy4+xOP4io+yHeuMOA6Yyf2Padp/r4+Ut76EjFmY7zcwn8MIp/lyfMVvmubRnkEtYx9bt8zZL8hDLiY2lyjhBCHEojIyNce+213H777VxwwQUAfPrTn+b+++/HtveW7X/iE5/g2muvBeAHP/gBX/nKVxgcHGTFihXceuutnHPOOQCEYchf//Vf88Mf/pBarcaFF17IZz/7Wbq7uwEYHh7m1ltv5cknn8Q0Ta644go+/vGPY1nx7/7169dz++2309vbS0dHBzfeeCPXXHPNkXxL9unor1DisDjYhX/8RuzZ7WNUvLg0ZkFbgs5MguGKS7EWp58aBqRtk6sndZt/+2nddGYdyu7sToa0jlr9PAylSDqKojs1lfdICxonaRnHJIw09caYXa2h4kXYjb4o020kE5Yik7BY2JbED0O2DVWmBD8uX7v4hN+YSc23EGIuOdQZCftay5/fMcYn7n+evtEaCXNvZbShFGEUB/EV0Jm2sS0ThabmR+STFiPVuGdJLmFR1D5eEBHo+PBjNmWtWmvqXsgoHvOyCV4bqjBccSnUfEwDSvW4iatpKHJJE9s0uHBFFyd1pNkyWD7q616zjFgIIeaqp59+mk984hNs3759wtdfeOEFbrvtNv74j/94ymOeeOIJbrvtNr7xjW+wdu1a7r33Xm688UZ+9atfkUql+OpXv8qjjz7K/fffTy6X49Zbb+WWW27h61//OgAf+chHWLBgAY888ghDQ0PceOON3HPPPVx//fUUCgVuuOEGPvzhD3Pttdfy1FNPcfPNN3Pqqaeydu3aI/Ke7I8ERuawg134mxuxRzYN8rcPvUpHOsGCtgRKKU6el6ZUD1plJn4Yctay9gmP3z5apT3lMJbyGa3uf+ysUiqeANNgmiZw9AMjTRUvJGHEm8tCzacZB2kGRFK2QRhFGChMw8A0NIZhsnpBjk/9P6eTSVjTbgJlY3boT1iFEOJoO1QZCVGkJzzHmY2gyrbhCs/1jfHtx16jb6RKLmmTtM1WE++0YaC1bjVc98MIQyncMMI2DJK2QXvaJuUYe/MeFegwmtKXZCYL2xIYhkmx7rOnWOPsZe2MVD12FWqYStGWskkmTLwgYtdYnZRjMi9rc8dPX552lK9kcAghxKHzgx/8gHXr1vHnf/7n/Lf/9t9aX/c8j1dffZU1a9ZM+7jvfe97XHbZZZx33nkAvO997+O73/0uP/nJT7jqqqv43ve+x0c/+lEWLVoEwKc+9Sne/OY309fXRxRFPPnkk/z6178mlUqxbNkybrrpJv7qr/6K66+/ngcffJD29nauu+46AC666CIuv/xy7r33XgmMiGObYSjesmo+//7qIC/sKLCgLR7fqxobHq01vQPlaU/zS/UA2zRY1Z3l6e1jrWyQmdiGImUbjDZOtxLTlygfVXGzuoj2tIMXhJTcvc31an6EZSgsU1EPQxLK5I1L2/nz/3jqCVUWc7Ck5lsIMde83sD3hv7ClBKajnQ8sne04vHqQIlyPSDSOs4MaTRHz6fiAH4mYWNbih0jNepBRNkLUSgSlsYwElz7xiWs7yvQO1AmZZuNktmQGfrGTpC2DUwz3j6mHYtCLaDkBqDj6+jKJaj7ERU3xDQUiztS+GHEd5/aQXcuMWWU78u7inTnEoxW/SkBE/n9L4QQsXK5POHvjuPgOM60933zm9/M5ZdfjmVZEwIjGzduJAgC1q1bx9NPP00ul+Oqq67i+uuvxzAMent7ueqqqyY8V09PDxs3bqRUKrF7925Wr17duq2rq4t8Ps8rr7wCQHt7OwsWLGjdvnLlSnbu3EmxWGTTpk0THtt87vvuu+/g3pDDQAIjR9Dk05/michMXz/aDvY0v9k3wjIMsgmTYi3YdzcOpRit+uQSFpGGsdr+s0xaD2U2LVBfv4RtYRpxzXbCMmnP2OwuuK2skSDSBJGmK+PQ3eYQRBGb9pRIO+aMP0/PC/nBc/30j9VY0p7ij89eguPsOyp0rP5beb2k5lsIIWIb+gus++UmRipeK4iwu1Dn168OAdDTnQUNmYTJaMVnpOIxL6NI2CagSDsWbhAxP5dkt+lioLEMQMU9RgZLLo9sGuL/c/5JPNc3xvM7CkRa41gmoY4wVTyafrp2IwpoTztxRkqoCaMINwgZq3hU/ZCkZbK6O4ttxQ3MbTPeBzy5dYTRqseZS9pa/aSySYt5gcMTW0fYMljh/OWdpPNxP5Ln+8Z4dXeJq85bytnL2mU9EEKc8C655JIJf//gBz/Ihz70oWnvO3/+/Gm/XiqVOP/883nPe97D3/zN3/Dyyy9z8803YxgG119/PZVKhVQqNeExyWSSarVKpRJPFUun01Nub942+bHNvzcfP9NzHyskMHKETHf609Od5exl7a0mnMfiScnBnOY3+0Y83zeGIg6woCd2yW+yTUVb0kIZio60g2MajFY8Xhup7jfgYRswzVTBw8JU8dSY1oCZCmQTJtmEMaFcaKjiMVL1yCXr9O4psWphjlULclN+nn//q01865GtFBsnfoZSfPFnG/n/vuUUbv6DVdNew0z/ho6Vfyuvl5QWCSFOdFGkuf+ZHYxUPHq6475LWmt2F2tYZvy/B0sukdbkkjZ1P6TqRRTrPl2WgVIKy1BUgpBX95SpByFWoxkrQIjGC0I29Bf5t5cH+J/XnMW2kQpfemgTG3cXKdYDTKUwlEIpKNR8yvUAyzToTFuMVOPx8hUvnjoXaY3WsHW4Ss0L4+wT2ySX3NvUr1Dz4owSoFALaEvaoOJ9wdbhCkqB0RgFbBoKL4yn12waKNM7WGF1d5aeBXNnrRNCiIPx8MMPk83u3SfPlC2yLxdffDEXX3xx6+9r167lve99Lz/5yU+4/vrrSaVS1Ov1CY+p1+t0dHS0ghq1Wm3K7ZlMBq31lNuaf89kMqRSKUql0rSPPVZIYOQImO70p+aFPLFlmB+t38n8XIKV83MTUkv7R2t8+O2rDusmYLbZB2uW5DltQY5HNw8xWHKZn0tw8couLMuY5ln3Zpq8uqeEF2psQ6FRKKUJwjgIkGik7s7LOnzyP53Gss50a+zftuEyN337WdzGqN/xV9QMlpjARSvnMVoN2DJYxjIN6l6Ad5jG/RbrIaYRN1XVGtxQU3JDTCOiI21T9ULcRnPWSMebP98xQcO/vTzAw68M8kdrF/LB31/FNx7dwpce2kQQaSxTYStF2MiU+dJDmwCmBEea/4aGyy5tKYdEyiCM4Pm+sSPyb0UIIcTht2240srQbDajLtXjcbppJ96yVbw4yBBEmraUgxe61PyQmh+StE1cP6QeRLhh1Jyzi2WoZiyCIALXD/nt5iG2jVTo6c7xgUtW8qWHXmX9jgL1ICSfsgk1OJZBOmFiGopAg2MpCjWv0U8rznhNWAY1P6TqhWQTcUPXppGKx8u7CoxVfUCzcXeRgVKdFV1ZLFNRrAVkExZ1P+6FMlLx2NBfwA3i5wob6+SR2hcJIcSxKpvNTgiMHIyHHnqIoaEh3v3ud7e+5nkeyWQSgFWrVrFp06YJj+nt7eWtb30r+XyeBQsW0Nvb2yqJGRwcZGxsjNWrVxNFEWNjYwwNDdHV1QXA5s2bWbhwIblcjtWrV/Poo49Oee5Vq6Y/ED4apv9kKw6Zyac/2WRckpFJmARhPMI1CDXZxsYjm7To6c4yUvH4/jP9RLNpD38QNvQXuO3HL/Hpf32RO378Mp/+1xe57ccvsaG/MO197/jpy3zzN1v5l9/t4Ju/2codP3152vs2rVmS5+pzl5JLWKQTZtzpPopHBOaSNovzSc49uYP2lENnJkFPd46zlrWzYn6WfMppddNvlso0/zSDJFrFG7Y1i9tY0pHCNuON3+Eqq7EMsMw4F3l8enEYaUarPqaCjrQ9IYhT9UIe3TzMSMWjb6TK3/3bZs7//EP83S978SONAYQReIEmjOL/M/qR5lu/2Yo3rtC7+W9ox2iVUj3g5V1F1vfFddmlesCO0eph/bcihBDiyCjVA1w/IjWurNIP48ltltEIcDTKZapeQMIymJdxsAyFF0SU6z5lN6A9bUFjyoxtKlAqnjbT6EWiVByM//UrA6zvGyPtmHz47au48JROIg27i3VKdZ/2tM3qBTkUCj+MSNkmSilCHZePmqZBJmFjKjAMqAchYRRRrPlsHarw7PZRio3y2KRlknasVvBjuOy2epCZjT5dW4fKuEFIW6OhbKTBMY0jsi8SQoi5TmvN5z//eR577DG01jz77LP84z/+Y2tU79VXX80DDzzA448/ju/73HPPPQwPD3PppZcCcOWVV/LVr36Vvr4+yuUyd955J+effz4nnXQSy5cv57zzzuPOO++kXC7T19fHV77yFa6++moALr30UoaGhrjnnnvwfZ/HH3+cBx54YEpPk6NJMkYOs+lOf6BxAlQPaEvZFOtx47Jm6qlSikX5FJsGSmwbrhzy8oKZMlimO5E5kPtOdtaydlYtyGKbJpYBXqhxTIVjmeSSFhU3pG7HWSLjlesBCoVunG41NYMkzXfxteEaYQQdKZs9RZfqYayrsYx4/G6xFhBMsymr+hH1IKIj4zRSm+PAhiYe+WubilzSolAPWt+TbgR/aPy70I3/KFR9fvBcP9eefxLQmECwfYzhit8awQgQas1INT65e3b76GH5tyKEEOLIafboqnlhqxeHbcbZGcG4IMLJnWm2DlUo1Hwc06AtZbN8XoZizacz43DG4jbufnQrSoEbRHFAX2tQisakeYJQ8+0n+vjZiwON5q4OGs2i9iR7CnX8MGK04mGgOO/kDiId8WxfoTUBByDRyBztyiY5eZ7J5oEyD720B60UFTdojOwFyzCwzHhaTsIyqHgBuwp1DKWpuAHzcwnQtDJjlIoDMaahsBslQodzXySEECeCSy+9lE9+8pN85jOfYc+ePXR1dfGhD32Id77znUA8KebTn/506/aenh6+8Y1v0N7eDsDNN99MEARcd911VCoVLrjgAu66667W869bt47Pfe5zvP3tb8cwDN71rndx0003AdDR0cHdd9/NHXfcwbp16+js7OSWW27hwgsvPNJvw4wkMHKYtU5/8hObajZPgFIJk7Ib4gcTP9SnHJM9xbjO9lCarn4Z4iZoPYksvQNlvv9MP2csagOY9X2nK8FZPi/DqgU5XthRmPB4iCOWuwq1aafaZJMWoY4mBEVgYn8S0wA/DHm2b4wg1LjBLFrpvw7VQFMte/u8T6TjtOGUHZcIjVV8wsY3EY82DsgmTKJIU/Wj+PRu3DfZfHsiDTtG9zYiKtR8do7VqHghrh8SNmq6VaORXsI22TlWo3AATWuFEEIce5o9ul7YUaAnEa+buaRFWyrOtNBa05VNsrQjRSZhsXmwxO6iS9o2sU3FRSu7uPLcJWwbrqBQeIFu9PBQKCMuBQ1CTXPH0Z62WN6VYXehxq9fHQRg7dJ2lrSneHlnkZGqR92vYRiKlfMzzM8kWD4vjWPHAZEg1NiWQS5hMVTx2Li7RD2KCKO41EcBRGDbirofUq77rbWu4gUkLQNDGSyflyGIdCszBuLRw/MyiVZpzuHaFwkhxFzWnBjT9O53v3tCKc1k73znO1uBksls2+ajH/0oH/3oR6e9vauri3Xr1s343GeeeSbf+c53ZnHVR4cERg6z6U5/YO8JkBvsPREZr+aFJGxjSjbF6zVTBgtMzVQBZn3f6U5vDnaqzYv9Bfxg5lRZDYQhmApOXZCj6oa8NlLBJcCdRXzEBA5nGKXmR7iBR0faaTWRayo3xhe2p2wqXoAf6r3lQuO+5ZGKz5bBcusEsOQG1BpZKIaxN4gSRJrAjRu4FiUwIoQQx7WZ1s2FbSkGii4AC9oShDreR+RTDt1tSa4+dylnjZvekjANLFPtDU5A63ShGRQxFSxsS2KquHTGNOK1vXegRNULqPkhNEpotg1XGCzWCSLNovYUXW0Tm/5FUcT67aPU/bg3iKkUxbqPZSqiKC4tNeKKHkIdr3c60hhELO1IMlzxyCXtuBzHD/HCiKRlckpXprXgHa59kRBCCAESGDnspjv9gThg0pa02DlWY3F7akKzsn1lU7xeM2WwNE0+kTmQ+07nQKfaRJHmiS3D7K8oJiLuzfHacJV6EDedsw2DvWNjZnZ4c0tikYbhikfKNunOOYxW/dZo3zDSjNV8UrZJ2lYUpnn/ntk+yvZ/rdLTneX0hW14jUa0BhCN+xYVzfciIuPI/52FEOJ4N9O6ecnq+URas7NQZ0N/gZRjsnZJnqvOmzqtRRmQS1gEod+YHKNbvbogXjvsRt+skhs3d7VNk3LdY9jb2/jcULrVZDXUUPVDnu0bJZuIgzIa2DFao3egxEAjq7LiBqDidTAI906kCzU4RtxrTOs4a1IpRdkNOX1RktGqBxpKXsCitiQr5mfpyMQBmMO5LxJCCCFgDgZGwjDkfe97H0uWLOELX/gCAOvXr+f222+nt7eXjo4ObrzxRq655pojcj37ypqwTEXKMbHMeGMwm2yK12umDJamyScyB3LfmaxZkueMRW2zmoCzbbjCqwPlKV9vZlWMN1rz6cwksE1FzQtbgYdjSc0PqQchnWmbSDNhtG98G7SnbNwgpNbokaKA+dkE+ZTDCzsKPLFluFXPHTFxSk8zRhJpzebBMr93SucR+b6EEEIcPtOtmxU34PvP9LOrUAc1cS2YrOKGdOfiKQMlNyCK9N7+Vo0+H81eISqKy1HdIMJtBEVgbzAjbGQ2lus+EXEfsH9/ZZB82iHSmtGKjz/uUCLUoMYFQ8ZTjWawXhDvf9qSFmM1H9sy+MwVb+CFHQXue2YHrh9hmwZhpA/7vkgIIYSAORgY+fKXv8zvfvc7lixZAkChUOCGG27gwx/+MNdeey1PPfUUN998M6eeeipr1649Itc00+nPhSu6OGtZnuf6xmaVTXEozJTBAtOfyBzIfffFMNSsmqWV6gFBuHfD1xwvCFODIynLaJx4aZK2EZ9SHYO0huGKT9I2WJhPMFrxW6N9dWNMb8Iy6MzYFGsBoEk4BtmkhV1WbNozMVA0/sRPjXuNwzOoWAghxNEwft3c0F/g7/6td28j9MZBygv9BfrHpjZCzyUtOrMO87IJdhdqjFS91gQY14/IJExMY+8a6gbxuNyZVpFmkEMRl3MmLIPBUj2eqKbiUfb1cSWwM69G8fQc04h7p9imgW0YbBmsYCjFH5+7lFULcrPOMhVCCCEOlTkVGHnsscd48MEH+Q//4T+0vvbggw/S3t7OddddB8Tddi+//HLuvffeIxYYgX1nTVy+dvGssikOhQPt+3EwPUJej1zSamzYFJGOT6lUvG8DNalSRim01o3mdHajB8exGxyo+xF7ii6daZtc0mK44rWCPm4Q4QYR+aSFZSj8QPPacIVnt49NOwWnqRmrioMkcoomhBDHqijSB7XWH0jT9ObzjT8EOXtZnj0lj7ofkrQNdoxU2FP0WNzhkEtalOo+UaQJwn2H11uZJBEkbZNQx2UzSdskl7TZXazv93vRWpN0rDh71TLxwwjbVIRRxMbdxdZ786n/dDrbGyPqD/e+SAghhIA5FBgZHh7mU5/6FF/5yle45557Wl/ftGkTq1evnnDfnp4e7rvvviN8hTNnTcw2m+JQOZC+HwfaI+RgNTeMhZrPqu4smwfLuEGjNnl8Xu84ZTfACzW5hEXCNkknTLzqsZk10jQhe6QtSbHmU/H2dj0p1AMsQ6F1xJahCn4UTVtGRONrpop7jiQck1XdMr5QCCGORRv6C6111PXjdbSnO8tV507tDzLZbJumbxkqYyjVCib88TlLeHlnkX9/dYhoXHfvMIzHvvtBHKwnCgmimbNFxmv2HqkFEWnbxA0iIq2J9P77ewG0p21ySZtmDmjVC3BMg+Gyx7d+s60xaW3ve3PWsvZZPe9sHGxgSgghxIlhTgRGoijiz//8z3n/+9/PaaedNuG2SqVCKpWa8LVkMkm1WmUmnufheXtHs5bLU3teHO8OpO/Hgdz3YEzeMPphhGMaE3puwKRxvSpuYlrzQ/wwoi1pT2hKeqyr+xG7i3U60w7ZpMVQyWuN9g0izcObhsklLDpTDmO1+DRvOkEjjbk7l6A940x7HyGEEEde84P4c31j3P/0Dup+yOL2NKl8owxmR4H+0allMJPNpmn6lkGPLz20ibGa3wq8dKQdKt64wwINQRRR90MMpRiueAwUh/HCkOAA1k8NoOMxvUajyaq3j0ly4/lBFI8MjiKqXoACivWAtGOyqC1JOmEd0HszW68nMCWEEOLEMCcCI1/72tdwHIf3vOc9U25LpVKUSqUJX6vX62QyM/fF+NrXvsaXv/zlQ36dx5oDyVQ5XFktG/oLrPvlpr1103mTqhuwbbiCpeLmouNjAqaKr8VQioRl4AURXhBRrPkszico1YNjuJhmIt2YXJOwDBbkE5TrAcVxE2pKbkDFg86Mgx9E006vMVQ8WSDScQaNEEKIo6/1QXxPmVcHStS8kIVtSbwwImtY+yyDmWx/TdN3F+rsLtZRBpwyLxsHXtyAx7cM4wURF5zSgW2ZDJdcXhkogVJ4oSbUcbbHwfQtj/uVaCzDoB6EaD27Jyl7AaBwLINswopH+hqKc5a1k0vZwL5LhA7GdPuMwxF8EUIIcXybE4GRH/7whwwMDPB7v/d7QBz4AHjooYf42Mc+xqOPPjrh/r29vaxatWrG5/vABz7A+9///tbfy+Uyl1xyyWG48hPbTHXTmrixm++YoOOJK/UgwlSQciwc06DkBpiGojPjEEYRXqjRhjpugiLjuUHEzrE6HWmbxe1JhsteqzlrpGGo7JG2DRa2JSjUAmr+3tKbtG2ST9tkEyb/99mdrFmcl9RgIYQ4isZ/EM8lLdCQTViMVD0q/QXWLMnTmXH2WQYzPitzX03To0ZvDstQrFnUhjIMALRqjtuFbcNVzl7WzvOFGuVGgN0yFZ4fYRiNkswDXDzdIGKw7GEZioRl7rMX1ni2acZrvG3QmXHwgojF7al4DPFYDcdUOJZJLmm13pttw5WDPpg5mP4sQgghTkxzIjDys5/9bMLfP/GJTwDwhS98gdHRUf7qr/6Ke+65h+uuu46nn36aBx54gK985SszPp/jODiOlCUcbjPVTfthRN2PCCJNGGryKZtIx4EQL4i/7lgGuaSNF0SEUXx6pY7HqMg4o1WfqheyfF6KIILNg5XWbVU/ouq7dDSCICNVnzDSdGYdTl+UxzGN172BFEII8fpM/iA+WvGINGRsk6RtUqz7bBsq05HuQCk1YxnM+DKPfTVN3zpUJow0b1i8NygCcclKpCGTsBir+mwerLCn4MYlmxH4jUarulGOeaASlknVCwmUpqc7Q9UNJvTLmvFxpoEbRoxVfbIJi5oXsGWoQs0LCCMwDUg7FvOyDifPy+D6EaVpsiVna7b9WWTtFEIIMScCI/vS0dHB3XffzR133MG6devo7Ozklltu4cILLzzal3bCm6lu2moEQKJIYygwDYVS8cwVu1E+A7BmSRuGUhSqHsMVn5rnT3mNmRqXHosMIAgjOjMJ3nfxcr7+8GZe2lVq9VqBOHhim4r5WQfDUJy5JE9byiGMNHuKr28DKYQQ4vWZ/EHctgxMQxFEGts0SDsWhVpAqR7QlrKnL4OZpsxjpkbop3Rl0cDC/MReas3X9YKIYt1n4+4Sbji1kUhzDG+TQVzCui+GAsdSZBNxD5M9RRdL0eo3MhPLUORSNu0qLiPdNFAmDDWmqTAAyzQIo7gha1iMKNQCFueTcdbNQZpNfxZZO4UQQsAcDYx84QtfmPD3M888k+985ztH6WrETGasm1a0Oq4aRlyL7FgGrh9iq3GBDg2lenwSFoQRSdtgsuMlKALxt7ygLYllGKycn+WckzsxFOwpeewYrbU2nH6o2V10WZxPsiCXwI+gXA9I2Mbr2kAKIYR4fSZ/EM8lLNpSFiMVj3zKxjQUYaTxw2jGMpiZyjyma4Qeac1nH3iJqhv31/LDCMuIjwQ0muGyC0AiOX1gYLLZrJntqXiyTN0Pgbj8J+2YZBLWPgMMtqmwzUbJrNatFwtCTSZhYiiFqeI1LtJQrnl4GYeTOtIHPVFmf/1Zal540GunTLkRQoi5RT5FiaNmprrpINRxj5FGZohSimzCxAtCan6EZSrQmodfHaTuh0QalIJswsQ0YJpDseNCSHzi5gYRFTfkXWcvYftwlaRtcvrCHE9tG2WkujcrZmehznd+t4M3r+wi0prVC3IsnzdzU2EhhBCH15QP4kqxoitL1S1QqPk4poFhxB/+X9xZnLYMBmYu85jcCD2KNB1pm8e3jGCoeOKZF0Sg4iBJoBulMrNsjjqbe7lBRNmNgyEVN0BrjdZQ8QIsAxQKf1LqiCL+nv0wAhR+qFEKDKM51SbCsUyUUhgG1PyQfNLCsQx+uXEPj28dOaiJMvvqz6K1Zlehxtql7Qe8dsqUGyGEmHumHrELcYQ066Y7Mw69A2XK9aBxkqYJI01byqY7l2xMntGkHYtswsJQipofUfNDHMvAtuKskqoX7XdXdyTPckxjQvLLrOwYq9M3WiGXtFqp0z0L2nBDzZrFeVbOz7RO3ACqXsiDL+/hpV1F3rqqS06rhBDiKGp+EN9VqLUmtXRkHNYsydORtim5AWjww5BTurIszCenlME0pRxzvz02XtpVZKDk4gURFS+kHoQEUYQfRPihxjbiEpXyLPp/zFbFC3GMOJjhBhGmobDNvbGXhKVIWkZr7VPEJbGR1q0y2UjrVomsZRhYpsIPI1w/JAjjZusr5ufwgohv/WYrT20dAQ3zsg75pM0LO+IGtxv6C/u81pn2GeV6QO9Amc6Mw5XnLjmgtbPZXPeFHQXaUw7LuzK0p5xZX5MQQohjk2SMiKNqurppx1Is7UgDmrVL8pS9ED+IsC2DlKn41xd2YShYlE8SaRipeNiGwjIUFTdsbcami5EoBRnbwAt1q/nc4WAoMJQi7UAtiNOCm4d2kxNaVOO6mgdse4ouCzMJgCmp00OlOs9sH+MXL+1h00C5df07C3VuvPcZPvS2Hq5/8wpsS2KeQghxpM3UKNU2DfIph+62JFefu5SzlrW3ymAOtsyj2eg1CDXnL+/gyW2j1MO4N5cyFEakSVgW3TmH3cX6rCfHzMauotdat0wDan4U9yuJ4qyVaG+lDJp4ulyTipM+sU2Fr+OyH4VCa93KAFVAGEUMlOpExbg/y56ii2ko2lIWp8zLMFzxuP/pHSRtg4obzljOMlN/lrVL27ny3CUHlOEhU26EEGLuksCIOOqmq5uuuAF/92+99A5WWJRPkU871LyQ5/oLRFF8ApewLbwgQilFs1y5ueFa0JbACzRuEKKUYml7klf2lIk0BDo+vfLDwxMW6c7ajFR8/FDjGCaOGWfAWKZBGEbUx71uM6NE672NYiMNdz+2jQ++PR4pPTl1+pLV3VzcM4/1fWPc90x/a3pN1Qv54s9e4UfP7+K2d72Bc0/qPCzfnxBCiJnN9oN4FOnXVeYxvtFrpDVJ2yDtJDCUItSa0YpHEMXlNBnHoOofujpTzd4MkbhxuG59PZhmaY10fD9D0cr2tAyFH4Ef6AnrsSYuLX1xZ4GoMerYsUysRhPbkYpHxQ1ZkEvw85d280J/AUOpfZazTN5nZBJxz5WKG7JlsNwKqOyvb4hMuRFCiLlLAiPimDD5wz8w7cZyYVuS/rE6uUT8T9c2Vasxa3PvooFCLe7F4YfxaN+qH5KyDdwgIgw1oT582SID5b19QCr+3vRlN5yYyjw5s6V5ShZp6ButTvvc4+uaDaU4c0kbSzpS/G7bKNVGqvSLO4tc8w+P858vPJn//oeryKdl9LQQQhxJ0wX8J3/I3tcY3l2F2n7LPMY3eh2reoQRpJNm4wO7puIaVL2QMIoID0MhabOnlx+BmsWKGhH3J+nMJDipw2LzYJlohszNCKgHGgNoS1qYZnyYEUWalG1Sqvu8UvVQwMquDIva09NO8xmvuc/Y0F/g3ie2T+kPcvaydp7rG9tn3xCZciOEEHOXBEbEMWu6jeX2kQovfHc9bhCRcgyUUuQSVtxkLoxaJ1h+qDEbE23akjalekAQabJJi5M602weqFD1D13N9cEwGk3qmpSCsJHqvKwjPeX+zbrmkYrX2kC7fkjCNLj09AXsLtZ5YusIED/P//7tNn62YRcfeMsK3nZ6Nyd1Ssf8w0kmFAghxpsu4D/ZbLJLZvrdMr7Rq20aKDSlug8oTCPu81H3QwZLbiujY3+aWRmzovc/onc8A8g4Fn4Q4VoR5yzL8+iW4Tg9ZAYRcTAi0BrXj4gApePDjUjHQZO2xrSf2ZSzTFlHG+ORn9gyzI/W72R+LsHK+bkZxyYfzik3Qgghji75zS2OaZM3lid1pFmcTzamtRgoZZCwTTrTDoWah9fIyrAMGpsTm4RlkrINyvUAP4jozjm8srt0tL6lFh2BMe7QKW5IFzeuu/7iUybcd6a65nTCIuWY9I/WuHDFPP7kvGX85YMb2VOMRzTuKbp87scv881Ht3LZmYu48tylnL6o7Yh9jycKmVAghDhYZyxqI3nBSby6pwzEE8ZWdGUxDLXP3y1nLGprleJYBozV/FZJSrPKw1AQRVN7WzU1SzibDiSWq4l7aYHe59Abo9kzRMOeUtwnxKp6jFUtZnM+MVL1WxmVzXLTpqoXsmmgzIquLB0ZZ5/lLDOto5mESRBqal5IEGqyCRPU9IGWwzXlRgghxNEngRFxXLEsgw9cspLbf/QSA0WXtpSNYxlEWrc62adsE9uKT2xMw8API6peQFvKJggjNg9WW5kZk03eJM70tUMhAoj0hM2eAq594zKSk06b9lfX3J52eK5vlD8+ZzF/+ydn8an/u4GtQ3vLcXaO1flfj27jue1jfPryM1iztP0wfEeN7+sAMieCIOLRzUMMllzm5xJcvLIL6xhoHDvT9zDd11/aVZz2BHJfKd1CCAH7DqoC+/3dctW5S/nd1hGe211qTHqJe380+27tL5vDmDTifvKY3X2Jm4nH97dNhTcpK2V8QKTJMQ26sg5uEDFQcmf3Os3/mC47RSlGKh5VtxBP/sk4M5azzLSOluoBxXq8RyjWA0puQC5pN55+aqDl9ZQ/CSGEOHZJYEQcd9559hIAvvbwZnYW6hTrAZapWNieRilYOT/La8MVirWAMAowDcW8jMNJ8zLsHK3RkbHZMVprdY5TxBu48c3kmuzGzk5rTXDo+ta1Xnf8Ji9hKa594zI+984zp9x3tnXNo1Wfh18d4rSFbaxZ3MYjm4YZa/RbCSLNU6+NcsP/eZqvXnceZy7NH/LN24FkTvzwuf7WzzAINZapWJxP8oFLVrZ+xkfDTN/DtPXn87MMV1yZUCCEOGAzlXW8sKPAjpEqKcfa7++WT7zjVMpegAIcUxERB3C11hMCEnYjADJ5GQsnfWFfmR+TNQMWKccklzDYU/In3D5djCWTMDEMg5RjkLF9xsLZlbSaBtOuwWEYkbJNan7I1qEKHWl7xnKWmdZRP4wII00qYVJ24yl4400OtBzKKTdCCCGOHRIYEceld569hMvWLJqQbbCoPcltP3qZpGVy7kkdlOoBfhhhm0Zj0k1IZ9bhv1y8nJ1jdV4brrQ2jkrF3T6icSnBaduMm7y5PkopHFPjh4cue+SKMxfQs7CNHaM1lnWkuf7iU6ZkijQ165qrbkDZDaj7IUnbZGFbEmWo1kawWPN5aVeRjrRDdy7B/3PmQp7rG2Pj7lJrk7qzUOeqf/gt17/lFG78/ZXkUzM3Zz2Q7I+9m3wXUxmAxvMjXtgxNiVz4v8+u4PPPfASNT8esdiRsnGDiO3DVW7/0UsARyU4cqD150+9NsKusTpvWJyfkskDkEvaPLN9hEc2DfKWVfMlOCKEAPY/9nVDf4GRqsd5yzr2Of3kB8/1M1r1mZ9LYBoKL4hAgRfEgfLxj0FpTNUInByi70MDURThhrPL9PPDiLof4Af6gPp8zRSwiXT8nGnHolDzKdZ99hTdactZxq+junEttmlgmXFPFjeIMA01Zdz9dIGW2TTXPZ5IjywhhJDAiDiOWZbBJad2t/4+YfRhd5a2lN26bXzt71t65vPW1YP863MeZTcgjDRRM3tEga3i3ibZpMU5y/Js3F1msOziBXs3cQfScG4me0p1OttSvOei5fs9YVo+L4Oh4Jcb9xBEcfDGMBRpx+T0hTn8ULN2aTttKRvXj6j5Ib/eNIgXRGQck5M6UuwYq7ea6gWR5h8e3sKPnt/F7e9cw8WrurDNiZvBA8n+aG7yewfKDFdcan5EFGkMQ5GyDeZl/FbmxPP9Y3zugZcYq/nYhqJUD3GDiFzSprstwUDR5esPb+GyNYuOaFnNwdSfL25Lsm2oyq5CjcXtydZjRioeW4fKFGo+FTfkbx/axL+/Oig9R4QQwP7LIzvSDttHqgQzRASaWQz9Y7W4saoNxXqAF0SNDMeJWQ/N5qvRgaSEzJIbaLIJMNXEspnpDFd8wN/3nSaZnF05fqKbJl7Pko1g0JbBCss609OWsyyfl6EjbfP4lhEMFWfLmIYilzRxLMVI2WNxe6o19Q723TdkNs11D9TRCFBIjywhhIhJYETMGbMdfWhZBleft4yNu0ps3F1Ca41lxhkOfqgxlGJZZ4pMwmK44nPmkjxhFNdD7y7WqXkh6YTFztHajA3t9mdpe5L5bZlZ96F44PmdrO8bwwsiDEORaPRVKdV8nto2yumLclx57hLSjokfRjy/o0DY2BjvLFTJJx2WdqQYq/qt0hqAHaM13n/PU1x57hL++6WrWdweb9I39Bf40kOvsnOsRtK2cCyFqRTrt4/y6u4SV523lLOXtbc2bduGK/x28xA7RqtEGhzLwDQNQq2puCE1r8qjmwf5xUudfO3hzRTqPo5lYJsGWoPrRwShR0fGoS1l01+o8ejmoQmBr8PtYOrPHdskaRuMVj1KjfuMVDw29BdwgxDHNMg4Jh1pW3qOCCFa9lce2Zx4Uq77tE8zcr2ZxbCkPQUqDsZCPFUmYrpeHHv7jhwKJmCacXAh1FD1o9d9WLAvCVvhBfH63Gj32iqtCcKo9f2fvriN/3LxKdP+jn1pV5GBkosXRCgF2UYAZLjs4YUa24qzR8pueFT6hhyNAMW+yrlkvRJCnGgkMCLmlNnW/q5ZkueWPzqDr/96M797bZRyo3Y4m7R448md/NlbVwBM2KSkExbveMMizlqW57ntYzzw/E6Gyt4BX+Oy9hRvXj0fYFZ9KIIg4msPbyYINQvzScpu2DgVjAMQQRhRcUNOW5ADwA1CijUPy1BUvZBQw2DZo+QGdGUTtKcs9pTirA6IN8r3P9PPv20c4BP/6TQuO3MR9z6+jWdeG6UWRPiNInRTKSxToTX0DlZY3Z2lZ0G8afP8kL6RuKltOmG1TvQspTAdk6ob0DdS5V/X72Ck6qFQWEa8wVUq/j68IK7h7kjHAYjBWTbmO1QOpv48l7BoT9vsLsQZRVpbbB0q4wYhbUmLYj1gXibBwrYkC9qQniNCCID9jn01G1lpozWfJVrPOP3knWsX88WfbaQaRqQdEwWtyTTjHeom4qmESdqxGC7Hv6ejcT27bFOhlMIPolZWx+ultcJUjddRqhWEMRtvix9qckmTtsT029pmRmAQai44pZOtrT5kGts0sE04bWEbK+Zn6B088n1DjkaAYn/lXLJeCSFONBIYEXPObGt/1yzJc9e157BlqDztmERgxue5fO1iFrcnueuhV6l48Qdlg4mN7XIJk5VdaV4bqVJyQ7pzCd60ohPb3lvis6/Rgk2Pbh5iZ6FOW8omaVskLBM/jMt/DAV1P2Cg5HL/Mzs4b3kHodaEkcYPJ57g1f047boz7bCkPUXZDdhTdFub1tGqz8fvf4H/89hreI0P9mHNIwwh1Bo31LhBY/qAH+CFIY9vHubV3SXOPSmPF8QnbpO3TwqwLQM3iHhlT5kFbUl2jtWJ9MTRkGajV0oUxUGfzqzN/hzKtOOZPqjYpjGu/hzcIGKk7MaTjxIWi/MpRis+uwp1gggKNR/HNCjWA5KWySldGWiccu7vZy2EODHsb+zr7mKdN57cSdUPJ2RAVt2AbcMVMgmLC07pZEexxvy2BGU3oOaFmKZqlc2Mt78SlwOhGtPf3CAEpUiYcPrCHL2DFTw/JNI6Hj9PvC7C6wuOpG1FSLxe6kbpa3N8L0AYaTrTNiu7s2zYWWTnLzdNCSSMzwjMJi06Mw4lN8APonjd0jBW97nuwpMwlDqipSxHK0Cxv3IuWa+EECcaCYyIOWm2tb+GoejpztHTnTvg59lZqHPyvAylus9w2cePIozW7k9jWya5VIK3rMqyebDCG5bkMafZ1Mw0WrBpsOQShJpEKt5iKqVwLBVnhtR9XD/ECzXf+s0WHultY7DkknJMwlBT8SY2t9MahiseFTdgSXuSFV0pBkoeZTdsbVw37CwC0Ja0mJdJkLBDhssezXZ9fqgp1gN6ByoYCvrHamwdqqC1RmuFnuZ0UzdGEQeBZllHild2lyi7AYZtYKi4YaDXCOR4YRww+aufvUKpHs7YhPVQpx3P9EEll7RoS1r0jVZxLIONu0uEkW7VptumwZt7uujI2DzXN0bFDck4JvMyCU7pytCR2ZsGv7+ftRDixDCb0s/JmYtbBj1Gqx5BpMl6Id94ZAsL80ksZXDqwhyv7CnhBYexnqXBMhSR1pTrcRPTtGNTqvv4YdQa99sMymvi3+evJzCzuCONaozldYMIP4gIIk1E/Hu4K5vk1IU5OjIOWutpAwlTMgKVapVEQhxc2VNyqbghZy1rP/iLPQhHK0Ax22l3sl4JIU4UEhgR4iA0NzIr5+fIJkyKdZ9C1QcVBxSqXsRYzeP6t5zCko4Un33gpRlTpmcaLdjUlXMwFJTcgLRjYZtx9sJoxSOM4rRiy4COTIJXdpco1uJylFTKpD5aY7p9cj2I2DJcZX7WYcX8DG1Jh+f6xii5ezdAxXo8Aac7l4j7k9R8xqo+mniTaxiQtEw0MFL1GuOONV4ISml0pFGGagVFEpZJOmEyXPFZ1pHmlT3F+ITTUHjjds1G4z3sG6nNOKHmcKQdT/6gkktamEoRao0fxeU0rh+SaEwxCMOInaMe6YTFBy5ZyeVrF/PIpkH+9qFNdKRtFrYl46PVA/hZCyHmluGyi6EU6YRJwpr4AXS2pZ9nLGrjFy/t5u/+rRe30R+j2JjAsnWoEv9esk3StoWOglZwYjYOpsQmCDUjFX9CtuFo1cdqBCHG93dVHNgI4Ol4QcRFK+ZR9uJSRjcIeaG/gBdqujIOpy/KEkSaYs0nl7SmDSTsr3TpaP5uPloBimP5PRFCiKNBftsJcRAmbGSUoi3l0DZu7G02qal6Ie1phxVd2X2mTM/U8R7iAMCvNg4QRJpyxaNc90nYZtzwLtJYpqLuR2STFsvnpWlLWmwbqlByAzK2sc9TOq1hoOSBBtUOp3Sl2TJUoezuzTKJNOwuuiQtg+62BNmExXDZo+aHVN2Qcj2cMAHBDzUTBkGGGgVYJixqS6O15unXRkhYBinHouYFU4IiXdkEuaSN1tG0E2oOZ9rxmiV5Llu7iK89vJnewTJBqDGNuN4/n7LxQ81YLWiUMcVTgbJJi/V9BS5fu5i3rJrPv786yAs7CixoY0JZ0f5+1kKIueOxzcP8/3+4gXTC5K2r5vO2U+ezsD1FNmGRdqxW9uBsSz9//MJudozWMI04Q8MyVLwu1D2qfkjVD0lZBlNqGWfQLP08mJhF8zGq8adZQhpEOu4DMu6+B9ugfLy0Y9E7WGFRPkU+7bBrrIobaFJ23OD7me2FVhZfW8ri5HkZXH9iIGF8RuBKJxP3i2qM680mzKP6u/loBSj2V84l65UQ4kQjgREhDsKBbGRmOy1n8kZ4fFbEaQtyvLSrSBBp/HpAqME2IIgUhqFY1p6i7IY4lkEmYVH1QgbHnejtS8ULGakGpGyDhW1JSvWAgbI74ZSvHkRsH6nFmRD5JBU3oFjzsRpTZSDeoDab/jU3zM3Ge34YnzIqW5G2TfwoHiMcaY0XBhiAbSm6sgmSdvx+KmVMO6HmQNKOl8/LHFAPkg39BX78/C5Stsk5yzowjTgItqG/QBBpUrZJV9ZpfW9eGFH1Ip7dPto6nTyYn7UQYm75h4c3s2mgDMD6vgJ//6tezju5g7ed1s3vn9rN/FyCjGOScqz9ln5uGSrzu9dGUIrGhJr494dtKjIJuxGshYofzTYugm1CEB18icvkoEjToexlAnGw/PKzFjFY9lpZNZFuBkWg6oWkHasVKBqpeBRqAYvzyQmBhOY6/PLOIr96ZZAwiks3Ix0HVFbNzx61381HK0Axfm+yaU+JtpSDacSThoo1j3nZhKxXQogTirH/uxycn/3sZ/yP//E/ePe7383zzz9Pb28vd999N/V6/XC9pBBHTHMjs6tQQ0/KE25uZFZ151obmWbK9JlL84zVPLYNVRireaxd2j5t2cfkrIhTF7WxrDNNpHVr4+lHzRM6xe6iy++2jfBifxHTAK0jXH9if5GZpG0DqzFyd1exhm0ZnNyZJu1MTesdrfr0jVSxTMVJnSna0zZhpElYxoR8aaXiwIFSYKl4cztUdjlzcZ6zT+pgXjZBpEE3Ur5NA+ZlHAxlUPebU3d0Y+qObk2oiSLNxt1FhitenKUyTY52yjFx/Yjn+sa47ccv8el/fZE7fvwyn/7XF7ntxy+xob9AFGm2DJZZ3zfGlsFy3Chw3Hu+akGOhfkk83NJ2pJW/L43mv6lHJOkY5FyLNqSNmEUsXOsRqExBvlAf9ZCiLnnD0+fOGo80vDUtlG++LNXeOffP8p//5fn+N7v+tgyWGas6uEFM+dWvLqnRLkeNMbLTvyQWvejCQHw2cYlAh03ls4lLRLmgX/w1RyabJDZvNBJ8zLcetkZfPaKN/Cpy07n8398Jl1ZB88PySXMeOR7Y1JbLmFSqft4QcRJHempz6fiSWMVL6RcD6i48X+/NlJl82D5SHxHUzQDFJ0Zh96BMuV6PC2nXA/oHSgf1oB6M0uy5oc82zfK41tGeLZvlJofcdnaRbJeCXGcGxkZ4dJLL+WJJ55ofW39+vVcc801nHPOObztbW/je9/73oTH/OAHP+DSSy/l7LPP5sorr+TZZ59t3RaGIV/84hd505vexDnnnMONN97IwMBA6/bh4WFuuukmfu/3fo8LLriAO+64gyAIZv3aR9shzxiJooiPfOQj/OIXv2g1YaxUKuzatYu//Mu/5MEHH+Sb3/wm2ax0uBbHr4PJApltyjRMzYp4bbhC30gVdHzSNz7m4fohSVvh+hGDZZcDKC8HIAQGy25j0g3sGI0zQxa0Jan7IXuK9QnPGUSanWN12pImC3IJlnWmqPsRxXqAoeJTzLakjVIK04jH8u4puQSN5naL2lOcl+6g5AbsHKvx/I4xtFIU6yFhFLR+bziWgWPGI4Ln5xKtZqvP7yiwY6TKQLFOZyYuVRrf4LTmxSnS9z+9AzeIpvQgeXlXke5cgtGqP6Fp64WndE6bieKFmjACy1T4YZwVYzc+SCilcEyDqh9SbARGDvRnLYSYe95z0XLOO7mTf3ryNX6+YQ+D5b3jx70g4tevDvHrV4fIOCZvWTWfS8/o5sKVXeRTFmnbmvS74v9l78/jJCvr82/8fZ+19qrepmffZ8BxGEBEIaiYIImKiIpbQhI1blEMib9gNAn5ajSa+POJUeLyoKho5OsGmohbXECCRBaRHQZmX7pnptfaz37u54+7qqbXmemZnmEGzvv1GrG7uuqcroFT97nu63NdM183pJQ4QXRUGR6mppGxdd7xwtVs3lflvx7cN691vvNFDNiGmOSq2T5cbzW0aeyrepPVIAFZU8cyNHaPNzvPaQvfVScgY+nqc8YUCNTmQs0J+NefPsnq3ixnLC2d8N/zSPNm5pu2SzJjGZy9rIQhBKGU1NyQHz60jzV9uUQcSUg4Rbnvvvv4wAc+wO7duzvfq1QqvOMd7+Cqq67iDW94A/feey9XXnklp512Gps2beLuu+/mIx/5CF/84hfZtGkTN954I+9617u47bbbSKfTfP7zn+fOO+/k5ptvJp/P8w//8A9cc801fOELXwDgr/7qr+jv7+eOO+5gZGSEd73rXdxwww287W1vO+yxTwbmXRj56le/yk9/+lPe/e5389KXvpRXvvKVALzgBS/gbW97G9dffz1f+cpX+Iu/+Iv5PnRCwgnlaBYyExd3h6qanZhhImPZGaPJpgxkHBNGcSftP5ZQbobKpTE19O4Ifo8wkoRR3BEywkgy3gxo+hG9OZvl3RlGGj71KcFvVTei6Tus7s3Sm7NUW43jEcW0BBENTRNEsbJ3R7HEbSs6rUaA9QsMtRvqRXioYNN2YKsbRNTdmBU9WYoZ82DYaiFF1QkYrXuM1j2aXsTGJcVOI8G+ioMXRmgC1vXnJ2WQ9IQWd+8YY/twg+et7CZTNDqCyWODVepuyKJietLvaekCXVN/X4jWPztmGokXRtiGTiE9uV74SJuREhISnp5sWFzgqovW8ebfWcnDAxVu3TzE7U8MU51wLW34ET95dD8/eXQ/3VmL3z2tj4s39PPcFV1kbeVMO60/Ry5l0PBCTF3rXNOCSOJHR+YMnIoaY5Q8a1GB3z1tAT96ZB/+0b3UcWf3mDPp65ob4rdCaONW6xktQV0TAomq3n1sX5VYShpeRLnps2V/DcePqDghXhgStUyHouVqHKq63PC/O/nEa898SkTsEy2oz5bXBdBfmLndJyEh4dTge9/7Htdeey3ve9/7eO9739v5/k9/+lNKpRJXXHEFAOeffz6XXnopN954I5s2beI73/kOl1xyCeeccw4Ab37zm/nWt77Fj370Iy6//HK+853vcPXVV7No0SIA/v7v/54XvOAF7NmzhziOueeee/if//kf0uk0y5Yt493vfjef+MQneNvb3nbYY58MzLsw8t3vfpeLLrqIq666ivHx8c73s9ksV199NQMDA/z4xz9OhJGEpwVHu5A5XNXsxAyTmqtECsvQWjPd4mB+x4RKRCnV4q79T0sXuEdQ3ZhPqUwSKSOQB8UUL4wZKCv3SF/OpmAbHKh6RBPUlzCWPDlUJ2PqdGVNFhXT1LyIihO0FpwtUUFKNE2QMqeM5whImzoNL1LjLDpoCGIOijXLutP85/0DkxZvq/tyNP0IL4xo+CHbhuucruXZV3WxTY0o1llcykzOIJGSHaONziJYogScdmjrI4MVxh2fpheSnyByWIZOxjKoeyGRlK2FuCSMJU0/xNQ0FhVTFKcIIwkJCQnFlIkhNM5d2c3Zy0q853fXct+ucX7++BD/u3UEd8IIzVjD5+bfDnDzbwdYVEzxkmct4KXPXsSzFuV5ybMWcMeTIzT9EMvQ0TWBF4aT6nnn0jATRBJBTNUNqHkhuqYhovikc40IwAsnKzZZW2eo5lLzVD5VBNASRTRUnXzVDfjH7z+Caeh0pS1MQ7BnrIkXxq3PO/U5oLWmQMMYwjjmV1tHuGPLMC9c1/eUiAEnUlB/qmqCExISjj8veMELuPTSSzEMY5IwsmXLFtavXz/pZ9euXctNN90EwNatW7n88sunPb5582ZqtRr79++f9Pze3l6KxSJPPPEEAKVSif7+/s7ja9asYXBwkGq1ethjnwzMuzCya9cu3vjGN876+Pnnn89tt90234dNSDgpmeoKWd6V4RebD/DlO3fS8ENWdWdJT3AttKtmNywqdMLY9JZLQdc1JMrh0XaLaEIt6EAtILWWbUTAEY/U1NyQUkY1rjT96XWA482AhhfRl7dZ3pNhtO5N2vEEaAYRTjkiY+v05Wy6MiZlRz3PC0KCGGxDoz9vT3pe1Q3wwpjevEUQxtS9qDNKk0+brO7N0vAiHh6osnjC4q07a7FxSZEdI3XGGj4Hqi7FtMmmpSXOWFLgG/fsIT0lI6XmhVQdNafvBjFBdPCGRAjBqu4sYw2fnaMNNi4pdo6VTxn0ZC2afkjK1Imles90TdCdsTB0wdnLu5Lk/oSEhGnYpo5t6nRj4fqqPebC9Qv4nTU9NPyIX28b5RePD3HPzjFVv95iX8XlP+7azX/ctZu1fTnOXl5iRXeGqhfiBRENX2UxtW9nVYW4PGJhQwJeFHPtL7awpJTuCAXzHZ56rEigO2MRx7IjVGwZqlNzQ4Ko1YLTEvSDlmivAQiVvxLFMI7P4mIax49otgJqdY0JYsDBHYGhmscnf/Ykv3xyuLNR8XSl40wtaNTcgCCMMQ2NvG2AEMetJjghIeHoqNcn5yBZloVlWTP+bF9f34zfbzQapNOTndGpVIpms3nYxxuNBgCZTGba4+3Hpj63/XX7+Yc69snAvAsj6XT6kL/g0NAQtm3P+nhCwqnEoZwfwKTHgijGDUJG6j5OoFpZ/DDuZGRMqpq9pNDJMNkxolwOfhgRSwjai2cJUhzcJbQNDUMXajcM5eaYjQnLQdwgQkpTbZvN8hQ/Uu6R7ozJkmKK/nxMzY0Yafid40ig4UU4XpPFXSl6siYZU2d/NSaWMTnbYNtIY1Iey47hBpom6C+k2DvW7LyOAJASS9eoeiFIpgkd3VmLrkwXFSdg12iTP3vBKl767IXsHG3w3fsHpjUGBWFMFKt8EF0TmPrk7Om0bdCVscjaxrTcGEMX5GyD3rzNwkIKXQii1hx20jSTkJBwJKQsnZSlQ1ZlQzX9iJefsYiXPGsBlWbA7VtGuHXzAR7cU5l0Kd46XGdrKxi0L2dRTJssLqVZ2ZNh61CdsUaAF0ZHFYY6MO4yWvMI4vikE0XafP+BQR7fV2VNX56UqXHzb/cStSriw/igU7JNDAgJhbSJbehU3YCq45NL6TRbYbVtUURKlSHVRkr1Z+JGxdNVHMmnDIIo5p6dYy0R6WDl8ereHKauHZea4ISEhKPjwgsvnPT1e97znjlPYKTTaWq12qTvua5LNpvtPD61KMV1Xbq6ujqihuM4Mz5fSjntsfbX2Wz2sMc+GZj3q93ZZ5/N9773Pd7ylrdMe6xcLvOtb32Ls846a74Pm5BwwplYpztTwCdSiROLimlcM+KhvRVqjhIS2k6D/RWX4arH6r4caxdkJ1lX2xkmN/1mD3vGm7iBWr21b+djmLSgM3StI3i04jBmpR0eGsaSjKVT90K8KMZotcsEs+w81twQCbx4XR+LulI8tLfCo4NVxpsHg0djYO+4i6ULFhZSrO3L0p21qbkhK7ozDNW9Th7LhsVFnCDiyf01YgmWoXVEh7oX8dvd4yzpStOdtWesRhYoYccyNLIt4WS26kPT0NAENLyQvrw9bbHn+BHdWYs/fv4K7toxOik35rzVvZy5rMgDe8psHarTCFQd8xlLipy3uoeo1XKTBKwmJCQcCW0nSRdqVKSUsXhtIcUrz1zMUNXl1ieG+MXjQ53K3zbDdZ/hus/2kQYrezJEsaSvYCMl1L2QuhviR0cukThBpGp7T0jFzNGxY6TB/XvKBJFyjYSRqtrVoTWqeVBQb39uqTphgRCCjGVQdSOKKYuhmvqsilvOyig++Jz2Z2va0lnVm33aZ2zUvZDRhsdY3acra5Gx9E7lccMtU8yYnLe6N3FDJiScJNx+++2Tyktmc4scivXr13PnnXdO+t7WrVtZt24dAOvWrWPLli3THn/Ri15EsVikv7+frVu3dkZihoeHKZfLrF+/njiOKZfLjIyM0NvbC8C2bdtYuHAh+Xz+sMeejT/90z+d8+8phOCrX/3qnJ8378LIu9/9bv7oj/6IN77xjfz+7/8+QggeeOABNm/ezA033MDY2Bhve9vb5vuwCQknlNlCy3Ipg7VWllufGAbgd0/rQwjBEweqRHFMKWMyVPMpOwFxK6cilnD/nnG2Ddc5bWEe2RrVAJVhcnp/nkf3VfnNzvFZKxKFADcIieXBEZrZNv80AaYmcMKYYtrg0jMWc+f2UWIpKaRMTF3V/zanJPGJ1nGCSBID/7+XnMaO0QaDZZdfPjnEjXftnrQg9yPJnnEHL7RZ1p0laxv86e+sIJ8yO6NFi/Mpfuf/f2tHoNFa76MhBJqp0fRVXskL1/bw2L76JKFjvOGzbbjG/qpHxtT54h3bO/bnmRqDRGvxHEslnkycqW6Htm5aWuLiDf1cvKF/xtyYSzct7nx/f8Xhru1jfP3uXTPmxCQkJCQcCbahYxs6pYxqrOnOmizryfCH5y5n50iDX2we4hebhxgoH9yJiyVsHznozrV0jULaYHEphR/GSiTxwsOOVEo4aUNX2+yregdFjwm/UIRq1zGEaAVuCyQtB4igszuga4IolhTSJrqmxJCW4aQjqLQ/DnQhSJv60z5jI44l37t/gJxtEMWq3agdwJ6xdMbqPrqu8eqzFz8tRaGEhFORXC53zK2uF198MZ/4xCe44YYbuOKKK7jvvvu45ZZb+NznPgfAa1/7Wq688kpe9rKXcc4553DjjTcyOjrKxRdfDMBrXvMaPv/5z3PGGWfQ1dXFxz72MZ73vOexfPlyAM455xw+9rGP8eEPf5jx8XE+97nP8drXvvaIjj0b99xzzySX35EwNTfpSJl3YWTTpk18+tOf5pprruGTn/wkANdeey1SSrLZLB/96Ec599xz5/uwCQknlEOFltX9iLj1H27dUyvOqhOSsYyW3VfihrKVCUJnRrruhdy/p8yqnswkN8Pu8SZVJ1CNMbOtciUcQc4qoI7VCGIsXfC2F67mwvUL2DbaYKjmdWbcwyjuLETb89qgLjS2obF7rMnu8Sbr+vOs6cvRnTW5b+cYjwxWJy3EJXCg5vGzxw9w5pICpq6xqvegKHH7E0P4sRqZCSOJodFp1wlb3/cjydoFBfZX/c577gYRD+8tU/dCcimTM5YWSRn6JPvzTI1B56/u4UDVZbThYxn6ISuWZ1oIt4Px1AjVwIxuoae7/TohIeH4YRkalmFRSEMQxXRnLU5bXODPLljJ5v01fr75ALdtHma04U96nh/FjNR9Ruo+KVOjmDbpypg4gcqIcIIjUz/mEuB6IpntnNwwJmWIjrDRXjNrQhDHEj+M8SOVXbUgb5GxVLuPbWiEsSSMJEIo8SSWklzKYGEhBTAtY+NQTXKnGu01zJq+PH4Us2OkTtUJO+M0fYUUpbRJ1k7GaBISnk50dXXx5S9/mY9+9KNce+21dHd3c80113DeeecBKgv0gx/8IB/60Ic4cOAAa9eu5Ytf/CKlUgmAK6+8kjAMueKKK2g0Gjz/+c/nU5/6VOf1r732Wj784Q9z0UUXoWkar3rVq3j3u999RMeejcWLFzM4OEhvby8vfelLueSSSzj77LOPy/tzXK54F110ERdccAF33nknO3fuJIoili5dygtf+ELy+fzxOGRCwgllYp3uVIIw7mxDtQM+o1hiaII4jjuWZSE4uKiKJaaummCG6x5LJ1TGjjd9drfyN3KWTkyr5UUINMAJYmIpyZgakZR4R6CQmDr8xUVrufJ3lX3tAy89nfff/BB7xx00oQQYAa2dM/V72IZOX96i4gQMVlwqjrIka5rgtAV5BLC4lKba9Fvi0IT3JJLcv7fCwFiTUsaiK2OStgyGax5IlRfSDhOUsQpftU2djKVTc0Myln5Q6DhQ58mhGk4QsbgrzareHN1ZZSecmNNyzSXPYsMlG6YtZB/bV51TxfJUDukWmpgT8zS1XyckJJwYTF3DTGvk0yZhFNOTtzl7RRevPnMJH/vJZsYaPsM1b5pg7gYxbqBcFlnboCtr0ivUNfZwozYnoyhyONrZKFEs0bR2m7qk0hpdDSOJaWhsHaqTTxmEUYwmBPmUGiMF5SAxNY1nLSogWtdtx486GRuHa5I71Zi4hslpBl2ZrlagbYypa2QsnV2jzSR4NSHhaUC7MabNGWecwTe/+c1Zf/6yyy7jsssum/Ex0zS5+uqrufrqq2d8vLe3l2uvvXbW1z7csWfi1ltv5cEHH+RHP/oRP/nJT7jxxhtZtGgRl1xyCZdccgmnn376nF7vUBw3KTiVSnHRRRcdr5dPSHhKmVinOzX3wjS0jsOiHfDZdnv4U0SLiY6wSApMHYJQ8usdo1x42gIAth6oE0QSQxdommjNQR+84dZ1iEJYsyBHytT57a5xIqmcKMparNwXE9P7V3al2LSkxIN7yli64LbNQywupam5IWEYUBd0jhe2FkqFtEEQqbntph9Sbh7csfz1jlF2jzsUUjr9hRQpJ2C06U+aW49i+PMb7+eK5y/nivOW05OzWVxKkbN15SizNEASx2rXNGfpOEEMSKJYsmFRgQ2XbOCOLcP828+fpCtjsyBvUfciRusepq4WsBPtzzPNRh9txXKbiW4hgKoTdBaTU4//dLNfJyQkPDUYukZe18inTIarHit7MjxvVTcjNZdfPjlCY4axGQmdkRpNQD5lsqCVR1LzVGtYdLhZm5OQqa4WSxMYuk7DV0HdpqHhBTENP+6MyUSxZOdok6xt8LrnLuU3O8cZKDvIWI2n5m2dDYuLrGh9Zkwcr6x7IZ+5devTyiE4dQ0jhKAwoXK+7oZJ8GpCQsJJw5lnnsmZZ57J3/7t33Lvvffywx/+kO9+97tcf/31rFixgle84hW8/OUvZ/Xq1cd0nHm/4n3mM5855ONCCCzLoq+vj7POOouVK1fOy3E3b97Mxz/+cR599FFM0+SCCy7gAx/4AN3d3Tz44IP80z/9E1u3bqWrq4t3vetdvO51r5uX4yY8M5kt4BOUq6OdlZGz9daCw2Cs4YNUoaZ6y7obSSUE6JogbWpkLYOqGyonRQsh1J+4nTY3Bdlyd6QMna60qSzBkfoeAuLWdtrExoEtIy4fuPlhojhmuO5PWmROlgdiFRJoaNS9CD/0VQCegJt/O0BfPsXGJUWGax5BJKk4IRBSylhYps5Q1cWfcOBISr521y5ufWKIv754PWctK7G6L8u2oQYjdf/gIt2LGGsoR0ra0rnloX1sPlDj8ucsVa+t6xi64P495Un230LaYEVPFi+IeXBPmf+4a9esO3xHK1q0d9pcM+KJA9VZj5/stCUkJBwPMrZOw4touBFp02BVTwY/jKl5IaMNvxPUPZFYQsUJqDgBhiZUxlMxhR/F1N2QxskeNDKBqcJIM4gxIommaQikcmpO+SEpZafevtIM+c93XcCvd4zywO4yt28ZRhPQk7VV3oYXsmOsQdYyOHdVF999GjoED7WGmSgKJcGrCQkJJxvnnnsu5557Lv/n//wffv3rX/OjH/2I//iP/+Czn/0sp59+Opdccgkvf/nLWbx48Zxf+7gII7MFpEz9vhCCK664gmuuueaYjum6Lm9729t4/etfz3XXXUej0eD9738/f/d3f8fHP/5x3vGOd3DVVVfxhje8gXvvvZcrr7yS0047jU2bNh3TcROeuWiamDHgs51XsbovCxK2Dat62hU9WSpOSKWp7M2GLjB1HT+KMXUopS2y9sFq2L78wUrr/kKKlKHjR2rUxNC1gzkcUYwQAk2otH7L1MmYOtU4JAbkLO0yAPuq7rTvTVxL6gLSpoauK1dE1ErxNzRBytTZOdrg2l9s4aqL1tGXt4mkpOlGSKmaYnqyNsu6MpSdgLHGZPFl77jDe7/9IL+zpoeBsSa2qbO0K81406fqKEGh/fOLimrW+d4dYzw2WOWSMxbihxEP7W0SRhG6ECCVcDRa96g4IaW0wU2/3YsXxPO6wxfHknLTp+z4bB9RbREZy8BoOYLGGj4VJ2RxMXXMO21Pp3n2hISE+WNlT5ZVfVke3lthQcFmtB6QsgQZSyNtpokkNNyQqhvghtNFkjCWjDcDxpsBlq6RTxt0ZS3cIFLC7wzPOZmYenaGrgLFDV1D16DuRhiaGjmNYvVZGUkQukBKyb27xthdbnLhaQu48LQFXLShvzMms33EZ7xVQ5+1Iz5761ZGGx7rFhSm5YmdygGth1vDJDX0CQkJJzuapnHBBRdwwQUX8I//+I/ceeedfOMb3+Bf//Vf+eQnP8ljjz0259ecd2Hk29/+Nu94xztYuHAhb3vb21i7di2WZbF9+3a+9rWv8fDDD/PBD34QwzD4/ve/z4033siznvUsLr/88qM+5uDgIKeffjpXXnkluq5jWRZveMMb+Ju/+Rt++tOfUiqVuOKKKwAVKnPppZdy4403JsJIwjHRrtOdLa8CmDSTvLiYoittsGusiRfGCNRuXz5lYBs6UsZUnYAVPVkuWNPbOc4Fa3pZ1p1mx0ijcwMupcrhsHRBEKvdqyCMyFk6fQWb5lg0bWznSJhYeehFEjMMqThqISpozbwbGv2FFBsXFdg63OC7vx3g6ovWEcetCkWhFqFDdY98yqCUMklbOuMNl/5CelKTwv9uG4XWa3dlTHqyNnnbZKTutd4jFd6qiypVN2TnaJM9Y02cIKLuBdBa8EqpXDW6ACECgtAknzJYuyA/bzt87RnzLftr7Gn9HeYsnZSpI4SGqQsKKYOhqoeftVjelZnz+z/1WE+XefaEhIT5Y+JN7YGKi2UIKk5I2tSpNF0kGhlbZ2ExhRCCuhdSdYIZBQ8/ihmtq7HIlKlRSKtmsmYrj2TWwO+TCCkFXiQJ4ghTEwSznHMQScI4IqpLnjxQZ+0ClXnXHq/82WMH+Pdbt+CGEQKouQHjDZ+GFxJGFVKmhqlrk0Ynpwa0nkocbg1zPD9rEuE/ISFhvhgbG+NnP/sZP/7xj7n33nuRUrJw4cKjeq15F0ZuuOEG+vv7+fa3vz2pX3n16tW8+MUv5o1vfCO//vWv+fjHP84rXvEK/uzP/oxvf/vbxySMrF69muuvv37S9/77v/+bZz/72WzZsqXTtdxm7dq13HTTTbO+nu/7+P7B/IR6vX7U55bw9OZweRVTH1veleEr/7uDz/1yG14YkbV0DE3g+GrhmrZ03nHhagxD6xzDMDTeeeEa/ukHj+H4EfmU3hFIml5EzjZ48wUreXBPha0th0q5GTAyZUTmSJn4HCdsZ40ACMJIErTqJOt+RNrUeGhvme8/MoiuaQhU6KpovUjVCak5IT05m/5ChkLK4LnLu3hiqDZpISlRNm8niOjOWCztStPwQkbqPg035ABQzFh06VpnZr5dsqCyVNRrBC0HddOPyKfMedvhe2SgwrW/2MJYwyefMslYBrEMaAYxQd2nO2uhayp7JWcbWIbG7vHmUe0gTjzW02WePSEhYX5o31BGseTy5yzl19tHeHBPhfFmQDkMSJsGw3WfWitXJGMZZCydRS2RpOGFVGYRSdqhrQBZS6cnZ4GEhh/R8EOOsCXxhKOJlqAvmOaQaWeMyFa+lpQq5NyPIrYP16m5IVlbR8bw9bt3smu0iWUIsraJoQncIKLhhYw3fH69bYSsbRC1xl8LaYOFhfQpncVxrJlbR0Mi/CckJBwrY2Nj/PSnP+UnP/kJv/nNbwjDkAULFvBHf/RHvPzlLz/q1pp5v5LfcccdXHnllZNEkc7BDINLL72Uz372s53vXXTRRfzbv/3bvB1fSsmnPvUpbrvtNr7+9a/zta99jXQ6PelnUqkUzWZzlleA66677rBZKQkJbdoVrkf62NtftIYFhRTX3b6NwYpL1Q0xdMGKnizvuHA1l521ZNrrtL/Xfk7Tj6c9Z+JiY0EhRdXx8Y5ibHziOI3VqjSUsfpdspZGDDy+v0bKaBBGEj+K+eY9ewDozVlU3JAwig+6ODRBxfFxAo1nL8zTU0ixpCvNg3vK7Bw7+N9hJFXDzmDFpWDrdOdslrTGayxd7dRJKYljSdTKLWkv3SSAEJhCErUWvgelpcnMdYdvagvNWMNHE4LenN2pwRxreBTTyvGyvDtDxQmOagcxabxJSEiYjRlvKPty/PmFq2n6Eb/aMsKjAxWGWg6Qdg183QsRAjKmTsY2lJMEJXhUnAB/BpFEiSERQkDONliQt4kiSd0PZ8wweSqZmGM1lbY2LlriSTuA/Fv37OFmfYCxus+44xOGMWPNoPWzOnEsEbpG2tKxDEHDlzR8tRGRTxmd0cl9FZezl3URtz6bTsXr8qHWMPNNIvwnJCQcLTOJIb29vbzhDW/gZS97Geecc860DdG5clwkbtednl3QptlsEgRB52tdnyFN8iip1+v87d/+LY8++ihf//rXOe2000in09RqtWnnl83OHij1zne+k7e85S2TXvfCCy+ct/NMSLjsrCVcsnERd24bYbjm0Ze3uWBN7ySnyFyfM3Xn5zO3buFnjw/N+dwmLjEX5G2qbogmBJahoQtUuJ8vsbIWuga61Bhr+gRhjLR0lhZT1DxlwTY0gW1oDNV9mn5EM4hZ1BrFOXN5idGGR22KeiOBihdR95qUshY9WZtiysCLJA0vRHJwtKctvKh/ap0dPj+SDNd9FkyoPZZSUnPDTl5K1j6ya8/EFhohBKauoWsCXdPozVnUWzP5y7szrO7N0vBj3DA6qh3EqceayKk8z56QkHBszHpDOVBhoKxuKC87awk33Lmdj/5oc0cAaCPlQbEDIGPpZC1DOUmgM24zVWSQUgVO19wQXQhyKYNi2sQL1GsFhxAlTgYmulwmnumu0QZrFxQYd3xcP8ILY8JYYhsCP4wZa/p0ZywsQwOhPmNiCUEsSYMKu3UDohgeGSzzoe8/yrr+/DG7Hk6lEZO5nmsi/CckJBwN3/jGNzpiSBRFdHd3c/nll/Pyl7+c5z3veccshkxk3oWRM888k69//eu8+tWvpr+/f9Jjo6OjfOMb32Djxo2d7/3qV79i+fLlx3zc3bt38/a3v53Fixdz00030d3dDcD69eu58847J/3s1q1bWbdu3ayvZVnWjI6XhIT5xDC0TiXvfD1n4s7P+gVZfvb4MZ0iQaAaWPwoboXZCWIpkagQ0liCpWv0ZHQ0DcoNHz+MCaKDOShNPyIMYwppg768clnYhsbiQoozlhS5f3cZZ4YdywglwjT9gJ5MkVzKII5jnJYQ1LZFtxfymojQNeWZ1gRU3aBzDmMNnx0jdapOQN2LKKQNbrxrN5efc/hFbLuFJl1UQko+ZVBIGwxVXbVQjtTvu224QbkZYOiC81b3HlWa/9RjTeVUnmdPSEg4Oo74hvKSAqsX5NB1DRnGhxylbPoRTT+COqRNnZxtsKikhOR6K7h1qugRSdlqtlEh3LmUgaUJ3DCmPkNd8MnAbKdUtA32Vxz8MKaUsWj4qpkniiFtCoJIUvNC8hiqQl5X3wsjyXhTCf2aEOQzBrqmwtQf2lPmyf01Lj9nKWctK81Z1DiVRkyO5lwT4T8hIeFo+Md//EeEECxfvpyXvexlnHfeeR1jxW9+85tZn3fuuefO+VjzLoxcddVV/Mmf/Akve9nLePWrX82qVas64as/+MEPKJfL/Ou//isAb37zm7n77ruPuZWmUqnwpje9ifPOO4+PfvSjaNrBXfeLL76YT3ziE9xwww1cccUV3Hfffdxyyy187nOfO6ZjJiSc7CzvyU2rNZwLGuBEUEibrUVyrGoQ24/ratSmkDIpuxGmpuGHEXUvVO6SVh1x01fP6S+mEZpqBRiueURxTE/WYnVfhqFawGjdQwqmzbG7oWTbcJ20ZVBKm/TmbLwo7rTXtIklnZpi29DozlpsHaqTsQy2Dtdw/QgJFFMGa/pyk3ZaD7XozNo6kZTsKzsU0irUtSdrs3u0qVwxusDQQNdgsOyQtnTOXFY8qh2vfMrANjUcPyI3g+PE8aNTep49ISFh7szlhrKYtsjZBuOhP8urTccJIpxgskiyuJQGqQTm2gwhrGEsKbdGTyxDo5RWmU5O0BJcTnIGKg5+rLJUgkhi6QJdgyiWRC1x3fUjdA0VLA6kTHVt39EKEO/OWMQoIclpNfpsGaqzdbjB+gU51vYfuahxKo2YHO25JsJ/QkLC0SKlZNeuXVx33XVcd911R/Scxx+f++7wvK+uN23axJe+9CU+9KEPceONN056bOXKlXziE5/g3HPPZWRkhAceeIA/+qM/4g//8A+P6Zjf/e53GRwc5Mc//jE/+clPJj12//338+Uvf5mPfvSjXHvttXR3d3PNNddw3nnnHdMxExJOdl591hI+/pPNnbnpuaChbvRLaQNNU2Gv402fIFILXoFaQBdSJrapE8dxZ449beoEsSSMYjRNUEjpSATlhs9Y3WXHaJOqExLFEl1TNYv9BYuMpTHcGrmZiJQw7oRU3JCRukspZU1rr5n080DG1vmHS57Ffz04yE8fO0DVCclaOqWMxareLF1ZCynlYa27jwxUuPm+veyrOFSdkJytfmc3jEi1fm8njDE0DU1oLC5ZGLrgwT0VLt20eM7iyMqeLGsX5Hh4b4W1dm7STZCUkn0Vh01LS0flRklISDg1mcsNZTFtsqI7g+OHOEeRBTKbSCKR1JyQmqeu3RPxw5ix1nU4ZSpRGiQNLzopq391oYK63SDGD2PqXqQaZzQNN45xfZXiLSXEjiSMJbqAvqJNzjbww5h8ygQhiKKYWEq2D9eJ4piUoRGEEX4U8dCe8hGJGqfSiMlcz3XiuE256WMbifCfkJAwN97znvecsGMdl6vPc5/7XH7wgx+wdetWduzYQRAErFy5ktWrV/ODH/yA17/+9Xz729/mgQcemJfjveUtb5mUCTKVM844g29+85vzcqyEhFMFy9J56wtX8emfb8GPZGsUhkPanVOmQKBmrG1DZ6wZkLEkKUMjY2md+XRDFx1RBCCM1evqmuC5K7sQqKyPlKmzsJBisOLy4J5x/nf7GKYmyNoGpq5qJOuRxAliVvZkWNGTpeoEOH7AaFO10rSJJfgRDDV8TCegL2+zuJSm7oaMNf3OYl0AedugmLH44/NW8MhAlTW9GsWMRd42Oml8h7PuTtwVW9uXY9tQnWYQcaDq4YQRxZSB1HXStsGq3iy9OZu8bVD3oqO2A0+s4WzvEKcttRu3r+LQnbV4zXOWPOWL44SEhBPHRCdZxtLYX/UmXF9tHD/u3FCu7Mly1vIS+6sOTsU7puN2RBIOiiRLu9JEsWyNJU4fn1HNNuq6nbF0urMWYRS3xlROjlmbSNIJkI2lEnaiMEbo6roqO//T+j8CpBAsKtpUnBA3jDB1gZQxTT8kkpIwiJAImpHKXdk61KAna+EE0WFFjfkYMTlR2SRzOdemH00at7EMNdY62vDYtLSUCP8JCQlHxItf/OJJMRyHIwgCPvnJTx7VsY6rLLt27VrWrl3Ltm3b+OY3v8l//dd/TQtCTUhIOH5c+bsqS+dLd+yg6oaAREOt+TShQkxpCRoZSydjGSAlw3WPMJaEUuL4PrqmskQMTQkPuhCTRnRiGRN3wlZ1enL2pPPoL9gqsBQwdQ03iNE1QV/Oxg0jhmseW4brygmSMkjbFr2ahttamEdTNh2DWDJYccnZBt1Zi6W2aq+pOiG2LnBDNQuvCYEmBItKGZU/MoXZrLsz7YplLIPtI3VGap5a6HshS7syrO7L0ZW1DvuaR8rGJUWuumhdZ0F5oKpuejYtLfGa5yw5aezUCQkJJ4a2k+yXTwwx3gho+hGxlGhCXbe7sia/e1p/52b48ucs5f6dY+w7RmFkIhOdJBlLJ2sblDImUayq1meq823nmAgBWcvA0AV+oEK4n2rklP8fAkRqhCZlCLxIoguNQtogY+qMNDwe3FvBNlRlfNML0TT1uSBjid/K1dI0NVppGyqUXNc07t89fkhR41hHTE5kNsmRnusDe8r85JH908Ztxho+wzWPh/aWWdOXT4T/hISEw/LWt76Vr371q5x++umH/dk9e/bw3ve+l0cffZT3v//9cz7WcRNGwjDkpz/9Kd/4xjc6wShSSs4991ze9KY3Ha/DJiQkTOHK313H2y9YzfceGGCg7LCklOaMRQXe972HiWJJxtLVjqSh4wQRT+6vIYQKWe3P20SxWuDqmiBrGbhhRCSlCmGVymasgujUDLY1Q7POSN0nBs5eViRrmwRhjGmo+t27to8Rxcp2beuqASeX0rF0jULKQBOwIJ9i+0hj2u5k3QtpeCHdrfaaQsokjmKaYUTVCVjWnZkxs0PGMftrHuWGTxhL0tbkc55pV6wra3FOpot9FZcH9pQRAtb15yikJwc1z4cdeGrD0MneTpCQkHD80DRBb85iYNwhjCWWoWEJjVBKqm5A0w/pyZmd68PGJUVeesYiHhqocjwkiE5wKwdFku6sSRC1zseLpjXi1D11U68J5YDRhLpWHqpq96kgbgV6a0KQtjRyKYNFhRQNP6LmBgghMHSNMFKbDHGsWshAdAJabVMnberomqDqhuwZazLenD3z5ViypU50NsnEc83aOjU3JIhiTF2dn+NH2IbGr7aOzDhus2lpiYf2lgFBueknwn9CQsJhqdVq/Nmf/Rn/8R//wZo1a2b9uVtuuYV//Md/pF6vs3bt2qM61rwLI3v37uVb3/oW3/3udxkbG0O2thBe/OIXc9VVV7Fhw4b5PmRCQsIstO21FSdgYTHF+oV5immT+3aNsb/qImMJCCxDkE8Z+KEKnkubGk4QE0tIWwYpU6fqBgihHCNZSyeOVf2trgl6sxYC5QbJWZN3kqSU7K842IZOXy6FrglqhARhzP6KS9kJiFuKR9kJaAYROc8gn9KxdJ2+vI6UkqxtzLhjJlHtNTU3pDdnUUgZlHSNQtqcMbNj10iDx/ZVafqtSmFd4y+/+QB/fuEaLjtrCTB5V6xd89te/C0s2PTmLfZXPPyp+SbzaAee2DA0E6dSrWNCQsLRE4YxP3xoH7rWuvmOwY9jhBBkTY1Qwo8e2s87X7imU9++vCdD2tJVS9hxHGGZJpJYBj1ZCy9UAdntUZw2cav+F1SzTftm3/GjaQGvTxkxlPImpqZRd0MerlXQNUEhbbK8O0MQxgxWHKSU+BEEocQ0IIiU+zJlaow0PPV5Gsc0JPy/t2/jLy/SZ7zxn+lzqv2544cR+you567snvaZ8lRkk7TP9e7to4SRpOq288IEhZRyBT1rUYEDVXfWcZs1fXnGmx5ve+EqShkr+fxKSEg4JB//+Mf5wAc+wJve9Ca+/vWvs3LlykmPO47Dhz/8Yf7zP/8TgD/90z/l6quvPqpjzYswIqXk1ltv5Zvf/CZ33nkncaxuFs466yye//zn84UvfIHXve51iSiSkHACadtrH9hdZrDs4EUxtqGRMjWGqx5uGGNoAl0TBJHKD/HDmK6MiWGb+JHfcopoGJrA0tUuUdoyWFRM0V9IoQvVPFNzQzU+I2DrcGPGbIyUqbO/6rK/6lJ1QrxQ7cBFKucOgDCOCTw1t111dcIoopix6c9bLCul2T5Sxw1nXjz7UcxgxaVqG2xYmCNvG9MyO+JY8ti+KkErTM82NLK2wZ7RJv/0g8cAuOysJZ1dsf0Vl/1VZ0JYrKCQNijYJuNGwL6KS8o0Trgd+FSqdUxISDg27tw20hkd9KOYMI6RCASg6Ro5XWOg4nDntpFOnfv6/jzFjMlYXbniToTkMFUkyaUMevMWbhBTcwLcKUJy2BLXQY1q5mydWEqcIJo2lnMiiYGaEyKE+nx0gxhDFwRRzO7RZutmXxADQqpa5DCS5GydlKVTnyAWxCgBaM9ok2t/sWVGB8fUz6mMZbCv4jDe9HED9bk92vB5bF910nOfivpbTROctazEDx4cxPEjCmmTtK3jhXGnle1lGzPsGVP/fybUuI2klLE4c1lpXs4rISHh6cull16KEIL3v//9HXFk2bJlAGzevJn3vve97Nixg/7+fv7lX/6F888//6iPdczCyGc/+1m+853vsH//fgBOO+00LrnkEl7+8pezdOlSBgYGjrhWJyEhYX5o22v3jjcZbQREUpIxdRpeyHDNU0GprXWUJtSCT6IWqk0vxGqFptqmpnb9YjV7nUuZXPH85Yw3fbYO1WkE0SQbLDBjNsarzl7M9Xfs4H+eHEbXIGubNP2D7o/2GlgXAtGqTHRa9brDNY+CrdObt1nRk2Vf2aXqzZ7fUfdCHttXBQkjNY+spXPJGYv45RND/OTR/fhRjKULbFMnnzKxDZ1CKmao6vGF27dzycZFrOzJ0pUx+Z8nRzB0NUduaIIwlow1fIaqHmcuLbGqL8O24cYJtQOfSrWOCQkJx85wzcMLYnxk54a7rbu6foSvxYBguHYwU2R1b46F+RSDZfcpOeeJIknW0imkTfoMDSeIqDoh/pTgKD+KO9+zDQ1T1wijuCOmHEv1/FyJoTXqqYJZw1i975YhSFu6OrdY0vRDdEPDiqVqtdE1Gp5yvpi6IIwkojWCunpBjqGqN6uDo50t9YX/2cadW0fxwpiUqbGomGJRMcVg2ZkmrDwV9bdxLHlgT5m+vE0QxdTciLqnRm0Xl9IYumDnaBPLEEn7TEJCwrzxile8AiEEf/M3f8Ob3vQmvva1r/HLX/6ST3ziE3iex8tf/nI+9KEPUSgUjuk4x3xV+vd//3ey2SxXXnklr3zlK1mxYsWxvmRCQsIx0LbXjtY9wkhZeQspE4CqGyBbooiuCWJJZ6Hth2qXrhlEpG2DZy0q0JUxqXlq7MWPYsJI8opNSjiYbYxjpmwMRas1RgjCSNUk6i2x4SASqUoAJs3G7y277Ku4lDImPXmbtK0zUvdnbTlo+DFfv2cnDT+m2gwwNEHFDZBAIWVQSFuYutr1U+ekRm/au64vXNfXeUxO2bpsf13KmFzz8g3sHm+esHGWU6nWMSEhYX7oyVlErYBrgXInoKYg0YAoUqGfPbmDeUdxLDlQe2pEkak0/IjGhBDWrqyJqQuafkzNDQim5Ix4Ydyp+U2ZGoYQLeHkxNlIZCwRQtXWE6m3O2/rgMALYzShRkfGW58vvTmbKIqpuiECiGKwDIEmtNaIp4kutEM6ODYsKtCTtVlUSrG4kMIy9U6T2kz18jNmk0g56TPbNuZXgGi7VNb05claGvtrHq4fkbJ0FuZtGr7aZFhQSLF7tJnUzickJMwbl1xyCUII3ve+9/GKV7wCz/PI5XL80z/9E5deeum8HOOYr5aLFy9mcHCQL3zhC9xzzz1ccMEFvPSlL502/5OQkHBiaC9cCmmLgbJLxjIQQlXwdhagAgxdI2plbESxVIuXWIIQrOrJdJpW8imzsyhrL2Ym5l/MlHUxddG3fbjOeDNg09IS+ysOQzWPIJZoU/YA44Onx9Tb+lDCSD3A0kO6czZLu9KM1v1OqN9EJPDV/91Nf8Hm9IUFMlkTK9BYWEjh+qHKNNEnB65ahkbVVY6anaMNxps+m5YWO6M/TktA6s2l6C/YjDV9do83j8iiPF95IE+FdTohIeGpZWkpg4bAbzn3tAkXyKhVwZ7SBEtLmc5zfrVtmAPVk0MYadMOYa17IZqAjKVaxQxN0PAj6m44LWekXasrUJXBQoAfRoRHmCp7NE4TgXpf5ZRzafgxDd9DSvV5abZslxlLp79gM94ISBshWVsDlJhjmzorerLK3RFGlJsBFSeY8bg7RxtsHa6zqic3zWkx0/V9ajZJuRmwfaRO1QkJoxgvilnWlZ7xM/JoabtUXDPiiQPVSWOmg2WHFT1ZvDDmBWt7+Ym7P6mdT0hImFde/vKXA/A3f/M3CCH48pe/zBlnnDFvr3/Mwsitt97Kr3/9a2666SZ+8YtfcO+99/LpT3+aDRs28IpXvGJeTzYhIeHwtBcudrolfLQWH7FsOzbozG8LoJgy0DSNKFZheZoG9dYi9XCLmSPNumifUzFjIlG7nkjJ1LiQuOUWad/zt8/V0ARRLIkBN4jYO96kJ2vTl7fpyqhduzCS+FHcEVcksL/q4QRlzlhSJAgjmn5IIWVi6AKBmLQD6Ydqjrwvb3fOd2VvliWldGcHzjQ08rZBJGHnSOOILMrzmQfyVFinExISnlrqfohtanihur5pE0TRWKrrqW1q1CeMJ966eeiIxYOngniCSNJxkmRMDF2j4YfU3XBSC5mETpCrJiBj6oDECeJ5H7HJ2zopq+3AiHHr6o30QyV0tN2WTT/quEVed84yfrVlhH1VFzdQYzc9WYvurM3O0YYaHwpjYiRf//UuTF2bdv2f6/V9YjbJQ3vLjNV9gjjGNnRovacAn7l167yNWOZTBkEU89DeClEcTxszrTghi4spzlpWYn1//pStnU/CzRMSTl4miiN/93d/x9e+9jW6urrm5bXnxV93/vnnc/7551Ov17nlllu4+eabeeSRR3jsMRVmKITg4Ycf5oUvfCGWZR3m1RISEo6Ftr02iumMqpi6QBNqLv3gDpra9dJ1NRsdRBJNg3NXdrO8O8PW4UMvZuaSddFeTD24p0wQxQihrOAzLWglardOb51rBC1LsNotDEKJG0qG6x51L2RZV4pctwoZrDo+bjg5aLDiBNy5dYSVvRmafkS1GdJfsllUSBPGkooTtEShgBU9WS5Y08vu8eYki3K+NYrUxvHCI5qRnu88kGOpdUxISDg1qTqqJraUMWn4EWEUq5FDoZxuWUtHtn4O1HXnji0jT+1Jz4GDThI6IklP1kJvO0m8cFIYa9wa+QT1OWEZKrTVm0EJOhrRpDmhRjiKDzpWLEMjbgn0QggylnKGuGHEGUuLXLppMX/z3Yd4fLDKqr4sUSR5ZLCKF0ZkLJ0gknSlLHaNzRzEejTX941LivzF763l/Tc/RDNQVbmxhJ6sxcreHF0Z84hGLI9UCFjelcELIxpuwIKCjRDKeWnqarRoqOrhZy2Wd2UwDO2UrJ1Pws0TEk4u7r333mnf6+vr4w1veAM33ngjb37zm/nbv/1bdH2yqHzuuefO+VjzunrO5XL84R/+IX/4h3/I1q1buemmm7jlllsYHR3luuuu45vf/Cavfe1reeMb38jSpUvn89AJCQkt2vbah/aUKaQMxpo+hZSa6bYMDT+SCCnxQ0naUo0zfhgx3vDpztn85UvWsXFx8ZCLmblmXSzvylB2fMYbHoau4Ucx0QznrmsQx2pHMG3q1P1INS9MsLkYmoYg6uwg7hlvsqCQojtrYmiCoZqHpQtKWTVKBGpxvGOkiaUrZ8zuUYcolCzuSlNKGwyMqwT9d1y4GsPQZqxPbHOkM9LHIw9kPs4rISHh1KKQNrENFUa6tJTGCaLO+ELa1Kk4PoauAk7b1x0xbRjx1GAmkaQ3ayEENDwV6DpR7IjkQSeJoanxlqj1+WaghPU5iyNCXU/j1uvTeg0pJZrQVLubpZMyNMoTxmIMQ+PPLljFtb/YwoGKy1jDxwsj0qZO049ImwanLcxTmkWsONrre9ZWI0n9hTSmLjB1JZ60n3+4Ecu5CAG7x5vYhk7ONqi6IZaudZydfhSTsw0sQ+uMmR6udh5OLndGEm6ekHDy8Sd/8ifTxscn8sQTT/CWt7xl2vcff/zxOR/ruG0rrl27lg984ANcffXV3Hbbbdx888386le/4vrrr+crX/kKjz766PE6dELCM5qJ9lonUHW7VTfA0jVojaWAQNfA1ATlZkAQx3TnbP7699ezaWkJ4JCLmZmyLmSrtjeIYvIpkycPVNk52mBlT5b/e88uhqoeUqIacGIVGjh1f09KWo0LrSpElJCgtTJSZKt2MmVqeEFMDPiRZGDcJWPpLMhbPG9VF24QU0hbLO3K8PBApdOO0G490AWMNX3KTkBP3ua0hQXe8LxlvHTjomnv4dHOSB+PPJD5OK+EhIRTi2LaZHExzb6yQ9UNyFgGaVO5AatugKmp9pJi2uxcd5Z12WwfaTzVp35MTBVJMqbeCZhteFFHEGkTxrKTUWLqAkPXEGFMMEtI92zkbB1T16m5IRMHdbxQIoloBhGGF2DoarSyK2PR8NS5tNtlvnznDraNNNAQBJGkJ2uzqjfbye6a6fp/tNf3mhvih5LFJRt9hmv/oUYs5yoE1NwQU9dY2Zdl8/4aVdcnjlX4b8bSWdufI5Yc8TjnyeTOSMLNExJOTl71qlcdUhiZT46739owDC6++GIuvvhiRkZG+N73vsd//ud/Hu/DJiQ8o2kvzm7+7V4e2F1msOzQDCJSlsHCooGuaR1bsKYJ1vRlefPvrOSMlihyOKbOQo81fHa0Qt+iWI3kIOGHDw0y1gi45aFBnCDC1NUxOzkjLbTWfI+haWgCIinJ2wZ+GKMJ6M6YNP2Iuh8SRrJz3sSS/nwK29DJ2DrPW9HF685dxo8f3cdDeyr05S1etK6PLUM1tg83OktcVeogeOG6Pi5c38tzV3RjWzo1N+iMzUx8D49mRvp45YEc63klJCScWqzsyXLmsiJVN8API5qtME1D1+jOWBi64OzlXazsyfLwQAUviEkZT68bNyknt9tkTJ3erEWMpOFF08ZogkgSREqssAz1uRKEMUdSbBOEEi9Un2VtYaJdBNQmjCGOY3wjxtKnj7f8yXkr2HqgTn/exrYOtsu0me36fzTX96MdsTwaIaA9Frtv1FH5Ki03T9sxsnOkwaJS+ojGOU82d0YSbp6QcHLyL//yLyfsWCd0EL23t5e3v/3tvP3tbz+Rh01IeEaycUmxM99bcQKqTkAhbVJMmyzvyhxTzezEhZgfxTwyUGnNUasgNjeIKLsBX7lzJ91ZiyiWmJpA14QSRVrOEDFhJ0/TIGvrLeEElnWlafoxhqYx1vBwW8GDuiaIY6nEHV3N3f/Bsxdy1rISF6zpxTBU181w1We84aPrGuv783RnbR4ZqHR2GZ0g4seP7mdf1WFRKc2y7gwjrdyS7oyFbeqT3sOaG2Kbggd2l/npo/t5dKDCq89agmXNLHwczWL1SC3FU89r4s/6fsT3HhhgoOywpJQ+5DkmJCSc/Dy2r8pYI6DcDPDCGFMX5GyD/oINiI6TAKDc9PGjiAMV/6k96ePIVJEkaxnkUwZRrESStjOwjT9BNLFbIol7iNDW7qxJ1Y3I22rUtO0GmXYeQM0JqTgBy7sykx4rpk2KGZOUZZC1dPZVXNwgImXqLCykDpkHdajr+0wc7QjO0QgB7YyRuhdOyhgBSElV1euH8bT3YyonozsjCTdPSEhIEvoSEp7GHGq+91h2PCbmmLRrCAsps7W4kXhhhKVr+GGs2l40gW3qKtVf14iiiKgV9hrLg38qTtBqXYBH99XoylpYGgxWgk7VcLtpR9MElq6xd9zh/96zh9/sHOeXTw53LLh/fN4KfvLIPgbKDpWmaj542caFWIbG9x8c7IzXPLCnwtu+9hv++PkreOPzliEl7A9c8im1sNVb7+Fnb9vCl+7YQdUNiaVEE4KP/2Qzb33hKq783XWzvkdHulidq6V4pr/buZ5jQkLCyc3EXfVnLy4wWHEoNwPGmwFNP+IFa3t5+4tWA/CRHz7G1gN1do85lJtPX2FkIhPHbbSWSFJIG4SRpO5Nr/9tO0sEkDLUTb07xW0yWvfxY/CimCiaXUBpf7/q+Owca7C6N9cRM7K2ztq+HLc/Ocxow8MJ4o7TMW1q9GRtXnzaglnzoI4km2Mi563q4bHBKo8MVFjZkyVjG0c0gjNXIaCdMZJNmVTdkIxloLda45p+SC5lTsoYmY2T0Z1xqM0MKSXDNQ8/iig3D44PJSQkPL1IhJGEhIQ5056FfnJ/jS1DdXK2upQEUUzNDTqO4ULa7LQHZCyDKA7UeIym5q5lfHBxKVv/o6EWuH4oKTcDBGp23DQEuhBIlCU6jCN0UGn/YYQfRTy0pzzJgrthUYFdow3qnrJFF9IGIHj12Uu49tYt3Ll1tHXekq/8705ufWKIv754PRuXFKk4Ps0gpJQx+fa9e/j3X2zBjySWoWEIjVBKyk7Ap3++BWCa8NB+j/aOOzwyWKErbZJLmRhCsK/qTlqszoel+LO3beHTP99CGB/5OSYkJJy8zLSr3q4P94OIwdZ1RErJv9+6VV0/Cilylk751I4XOSpiCTUvpOYpZ2HG0rB1HT+KaXjhpDEayUFBRGu1+0iphBNNF53F8aEqjwUqMNwJYn7x+AG+XtnFwwNVHD8ibenIOGb3WJNYqmuyZahrcs0NcfyInpx5zDfXEwX1uhcy3vAZqnmdQNZNS4pcfs7M4vrRuBrbGSOblhbZ1aohbgcB92QtlvdkqTSDQ7oq4liyeX+N0bpPzjaQUk4TR+bqzpiPANfZNjPGGj47huvsr7qkLYPr79gxaRMmISHh6UMijCQkJBwVG1sLrq3DdaJYMt70O7txulCLziiOVUBdyqTph2Qtg7LjE0btwuDJCAGmrpEydXKWxlDNBwGGQIkirSdomiCKJF4UEXmSIJJsHWrQk7VwgmiSBXfVhN2muhcw3gjoy9t85LKN3LFlhGtv3cJoXe2u7hptctU3H+DSMxfx9hesJpcy2Dfm8N3fDtBfSOOFUcdpYiEwNEHTj/jSr3bw9gtWzziykjY1xho+u0ebgLIKn7uim7e/aDUblxTnxVLs+xFfumMHYSzJWLpq8ZnDOSYkJJx8zLirLoTKQUqZ2KbBlqEaN9y5s3P9qHshDT9C1wXxkQRqPE2JYknNjagRqdGjlIGpa7hBTMOfXv/rBq3PLk0J8KYlqDnhIRtthFB5VUEUc9N9A5SbPmEsO269SjNAosT7WCqBXwg1BhWEMT96aD/vfOEaDEM7xFFmZ5qgbug0vJBqzaMZRKTNQ7/u0YzgtMWUlKHznOVdncD1dhNOw4twzWjWjJG2kPPw3gp7x5sM1ZS4t6o3R3crmBbmVj0/XwGuM4XfumHEQ3srNNyAnG2waUkR29STlpqEhKcpiTCSkJBw1Jy1rMT6BXmcIGL3mLrxz6dMYikJaioXRIskK3uyuH5E2fFV20wrbFWo/FRAteUYmqCUscjaOkEkO4/nbJN064Y/kpKxhkck1G6erqnn2obGWNNH1zTu3z0+owU3Z5ukDJ2xpk/TC3nhul7OXl7iS7/awfcfGOwsgm95cB//u3WUv/i9tdScgCf2V+kr2PTkLPJhzFjDJ4jU4tcyNKpOyM337+X5q3s6O1Z1L+QzrV3c5yzrIpKSuhsy3vRp+gd3webDUvy9BwZUdaKhdUSRNhPP8XsPDPCG5y0/6r/vhISEE8dMow4T27/0VqtY3YtY1pVBCIEfRDT98BQt6z0+BJGk4qhrrqVrlNJqRNJp5ZRMJIolFVd9z9QFaU0jjOPOKGcbAdAKZA1jyd7xZsf1CCpAfKLZpCdnIaW6Hpu6yuEaqDjcuW2EC09bMO2cD+eAmCqol5sBj+2r4oYRXa0NAieIeWhvmYHyzDfwR9OCM0lMWZCjkDY7jx2uMn6SkFNIUXZ8Rus+Yw2fhldh45JixwF1pNXz8x3gOjH8dsuBGlsO1GkGEYtLaVb35TqtQklLTULC05NEGElISDhq2oukHz68jyiOWzs+AtmyDjf9CISk7gWkLA3hgoykClFt2ZfdIEYItdCUqFDUjKVCWNtLDcvU8KOYYtrE8aOO40RIiGNlu01bOimpU3UDBssOFSeY8ZwNXWNBPkXdUu6RnG3wlxet4yXPWsAnf7aFHa2Ky9GGz4dueYylXWmCGMqNANeP6claLCqmqHsR400fXQi8OOZb9+7hR4/sVztWhsZowwNg09JSR/AoZSyWdKUnLajmI/BtoOwQS4khZt4h1IXAlzEDZefQf6EJCQknDVNHHaa2f8nWXXhX2iLdcoL5sSSKleistdpCnrm+ken4UYzfVJJF2tTpabWqzFT/O6nZplX/G0YxfqTe+c4YqFTjnVpL3AcljLRp+hE5W7kn21iGRtUNGa55087xSBwQkwR1YPtIHTeMKKZNQCCEEn76F+UZqnqz3sDPtQXnaCuFZ3JGru3L4/oVnCDE8UO2D9cxtTz7p4yazsbxCnBtj+HesWWYf/v5FroyJgsLqUmtQklLTULC05NEGElISDhqNE1w3upuvv/goFocRhJdU7tumgBDVwGpI3UfXRMUUyZjzQBLh+6MjRBwoOoqZwhgCIEfqt05TVN5IkLA0lKGA1WXihN0BBQAhHKMKLutQAi1I9gMIqqzCCNt2u6R8WZAwwt49uIi1/3xc/jWb/bwtV/v6uwQ7h1XYoLXajoYrLgUUgalrEXaTLG/5tH0I8pOwLLuLOmiznDNZc+4Q8YyGG8GkyzCUxdUR1u1OJElpTSaEIRSYs2wVxy1rN1LSulDvicJCQknDxN353tCi0cGqxPav2C8GaAJwVjDZ3/FZUlXGksT6Jpy03Xu46a484DJ19FnKMpVcbDZpi9nEUvww4jalCYaP5L4LZHENlRbjR/GxFIStcLDiSVuLDtOkjYSlJtRKMHCMrSW0CLoy9uTjnOkDoiJgnrNC6k6KghVtkZ7ojjGCyOCMD7sDfxcW3COplJ4JmdkV9Zi45Ii20fqjDV8DlRdCmmTM2d5nakumljK4xbgqrXcq5au0ZefLIq0SVpqEhKefiTCSEJCwjGxsJhmYSGFH6kFQjuIrb+QojtrM1L3GCw7SFQLgKmresmUaYCUGLqmdtuE2oOTEmIpsTWVKWLogjV9GfryNttH6ozUvM4uqKULenI2ttF2W6hGHNvQJ1l8Z8PQNfryNmlLY7yhhJQrnr+CC9f38W8/38L9u8udn41icGWMpaudvoYfUUoblNIWGUPn/FXdqnMYJc7YukYUx+wcqdOV6Zq0cJu4oDpjSfGoqhYn8uqzlvDxn2ym7ASqsWfCa8RS4ocxpYzJq89actj3JCEh4eSgE+A81uT+PWXClmsuklB1VWbThsV5HthTYfP+KouKNqahk7EMGn5IHKrrqaEJTEMjiFQzSpy4SCYxtdnm7GVFlnZl2HKgzraROl44vdmm7fNIm5oaYQqjwwa1mrr6THODiLobs6InywVrejs/MxcHxERBPQhjolgSxTEVJ+oINlLClqE66/rzeMGhb+Dn2oIzVzFlNmdkV9binEwXZSdg11iTt75gJS999qJprzOTi6aUNhmrKwFpJo5FuAjDmM37qpSbPrtGYFVvFjHlnOaSg5KQkHBqcHSJTwkJCQkt8imD7pzFaf15zlnRxdnLS61/drGyN8tp/XlW9mZZ3p1hXX+OYtpEbwkICEFXxuzsZkaxBAFBGDFU89SCsC/H9pEmpq5x1tIS6/pz2KbaccvYBppQoztBFFNxAkxNY3Ex1bIUHxk522RRMUU+ZaIJWNqV4f957SY+8NLTKExY9MRShcp6YYQThOwed9lXcVjWk6WYtchauloAGxqGrmHpGhUnnLYwm7igat/8dGct1SzQEpfqbsjWofoRWYotS+etL1zVCVr1I7VQ9qOYph9haIK3vmBVErw6B+JYsn24zoN7ymwfrhOG8aSv43jut5ZTX/NoXiPhmcXGJUVee84yDF1VlNc9dePbk7XZuKRITy7F6QvzRLHk0cEqAujOqp1uTRMtN54kjiUpQyNttq5RuhKWEyYTS9h6oMYvnxhi11iDtGXQX7Bbn1vT3y8nUNfYSKrRnIylM9PbqpwlklBKwihGCMGy7syk6/rO0QZbDtTIp0zGGj5VJ0C2RnKmOiCWd6nNgq1DNdwwIopV9pUXRGgCkBLbENS9kIf2VgiieN5v4NtiypnLSqzuyx3yM2qikCOlpOoEjNY99TsChqbRk7U4feH0sZe2i+bhvRVKaYuVvVlKaYsdow32V132V9wZj3m0wsV/PTDAKz/7Kz7248fZMdLgN7vH+fGj+9g1erDqqb1psW5B/rA5KAkJTwU/+tGP2LBhA2effXbnz/ve9z4AHnzwQV73utdx9tln83u/93t85zvfmfTc733ve1x88cWcddZZvOY1r+H+++/vPBZFER//+Mf5nd/5Hc4++2ze9a53MTQ01Hl8dHSUd7/73Tz3uc/l+c9/Ph/96EcJw1PHVZXInAkJCcfE1DC2qY6H/VWX563sBuChvWUKKZOxpk8hZXYS+pt+hBdEam5eqrGVFT1Z3nHhatb05SZZdi1DY3VvDjeI0DWouRFOy6XSnbEwdKFEmTkuVia6R8pNVSv8+89eyPNWdfP527fzs8cOdH62vTNoG4Ji2qQrYzJa9ymmTQppE1MXFNIGo3UPTQiC6OBW4kwukKOxJk+lXcX7pTt2UHVDfBmjCUEpY/LWF6xKqnrnwNTdySCKO04kU9eOqvVgvpoTEp55LCymWFrK0JOziGOJaWjkbaNj719YTDPa8FnZm6XiBFi61mn36s1Z1NyQmhvgh6oxRddUNpMQiTA3ExVvgvXDV//fNjS60gaGrrKz6l44aTRJSjoZJZpQTTSgbs5VQCuEfoxA5Yus7M0SSzlpzOPBPWW2HKiDUCJqu9FmRU+WpaV0xwHxwJ4y/3HXLnYMN9hXddk73sQNlRsoZWrKtalrFDMWli4Yqnr4WYulxTTbh+vHVGl7tLTXCXdvHyWMJNUJ7tJCysDQBeet7p32uX0oF83GRQWGql7HLaVpB/d65xLgOpH/emCAf/rBYzh+RCFtkrVUtk/NDfnNzjHiWNKXTx0yTyUh4WTg4Ycf5rLLLuOf//mfJ32/Uqnwjne8g6uuuoo3vOEN3HvvvVx55ZWcdtppbNq0ibvvvpuPfOQjfPGLX2TTpk3ceOONvOtd7+K2224jnU7z+c9/njvvvJObb76ZfD7PP/zDP3DNNdfwhS98AYC/+qu/or+/nzvuuIORkRHe9a53ccMNN/C2t73tqXgb5kwijCQkJBwTRxLGdvk5SwEVEuoEEbqmUXXVAt4LI7Km3ln4Xbiuj7OWl7hgTW+nxnCqZbfhhfx7q/FlaZeB3mqrqbnhMS9W2tkjZSeg7gaUMhZ/+7LTufhZC/i3n21hX/Xg7pQXqqrgqhOiCdU0UEib5CyDs5eVuHPLCFUvJIgkUSwPGVA3V2vyTFz5u+t4+wWr+d4DAwyUHZaU0rz6rCWJU2QOTJ3xd80pdY1LS3Oua5zv5oSEZxbt3XZD08hlZs4h6s5a/NVL1qEJQc0N2V9xuGv7GFuH6wgEXhgjRIwbKHeDbP1JmBkNJrXKeGHcqaPPWkpwEkI5MppeNGk0KZZ0at11TZAytFZmlsTSdQSSemvsse0mfGSgwk2/3UsziLANDT+McIOY8YbPvopLb85ibV+OIIq5+b69eGHM0q4MfTmbR/dVaFRcYqk+k9KWTiFloglB1Q3J2QZhHPO333uYobr3lAizmiY4a1mJHzw42BEd0raOF8YMlh3Sls6Zy4rTPu8O2dqmaZy+MM+jg1UeHayyqjd3REGwsxGGMdfdvg3Hj1hQsBGtMPNeIai6AQ0v5KGBCs9ZPrdNi4SEp4KHH36Yl73sZdO+/9Of/pRSqcQVV1wBwPnnn8+ll17KjTfeyKZNm/jOd77DJZdcwjnnnAPAm9/8Zr71rW/xox/9iMsvv5zvfOc7XH311SxatAiAv//7v+cFL3gBe/bsIY5j7rnnHv7nf/6HdDrNsmXLePe7380nPvGJRBhJSEh45nCkjof2zzywu8xg2WktAnUWFVOcvbxr1oXGTPPPE4/XCKI5OywOhaFr9OZs0pbOeMPHD2Oeu7KbL735ufzHXbv41r17OruFYw2fu7aP8uwleZaWMpSbAU0vopAyWL8wT9MPCaOInSONw57jXOe8Z8Ky9KSS9yiZujsJ8MSBKlEcs6BgU3VDdo01ec7yEmsXHFnrwfFqTkh45jDJlXeIHKLVvQfHGc5cVuLiDQv52WP7+fKdOxFC5WhoQhlNmv4hAjFmoL0XP7dnnboc6vdstGp+NQE522BhwSaSKkdjarNNFMtOJbCpCzQJlqlT9UKkgKytd64RXhDTlTbZX/VU41DrMyaMJAcqHqN1n768zcJCinX9eeW4TBk8mwJOa4RSE4K0peOHqs65J2tTypg8sb+GJqqsXZB/SoTZOJY8sKdMX94miGJqbkTdi9A1weJSGkMXPLinwqWbFk+6Dh6utW2iW6rs+Efltmxz57YRFa6eNjuiCIBt6vQaGilDBbtfumkRr3vusuR6nXDCqdfrk762LAvLsqb9XBzHPProo6TTaa6//nqiKOLCCy/k6quvZsuWLaxfv37Sz69du5abbroJgK1bt3L55ZdPe3zz5s3UajX2798/6fm9vb0Ui0WeeOIJAEqlEv39/Z3H16xZw+DgINVqlUKhcGxvwAkgEUYSEhLmhSNxPEz8mYoTUHUCCmmTYtqcsztiPhwWMD3pvm273TnaoN76Xt5WYYYpU+ftL1zN7522gH/92ZNs3l8DVOvLQ3ur7B5z2LSkiK5pbBmqsbiU4r0vWU9fwabhRWTtE2tfTpgbU3cnq07QaXsQQiNjGVScgJoXkk+ZR9R6cMgdz6TyMeEIONqKVIC7dowRxZIVPRnu21Uma5utcZDpNbGH4pkiiMyFuBWCW3VDTF2NhXZnTbxQBX56U9JYO/W/nnKFhJFyhOwUrWtEIcVwzSOWauQJ1FiO1jpWGEuGax5reieLY6ahYxk6KVMnlnD6wjy2oWEaGjlL5+4dY0RSsrov22k+y6UM1lpZHtlX5Qu3b+PK313LYMXpiC8THZvzQfs6uKYvT85WTTpBGHfGwupeNON1cFJr2wzPm8ktdbRrgeGaRxhJ7PT031sIlWnWDJTglHyGJzwVXHjhhZO+fs973sNf/MVfTPu5sbExNmzYwB/8wR9w7bXXMj4+zvvf/37e97730dfXRzo9ObA4lUrRbDYBaDQasz7eaKicnUwmM+3x9mNTn9v+utlsJsJIQkLCM4sjcTzMhytivl5rptyHrowJCMabfud7Zywp8gfP7qcnZxOEMWsW5Pj3Pzyb/3pgkC/9akdnl7DcDLhjywiLSimev7KHl25cSF8hhaFrLOtWAX5Tb44TTh6m7k4GrRBbo7UINjSBE0uC1g3PkbQeHG7HM6l8TDgSjrUi1Q+jzr/Lc5NEEo6EIJKMNwPGmwEpU6OQMrFNlUdSc4NO/XsbL4wZqvm8/gt3cebSEkEYU0gZuEHUGTGVHBx30jTImDp1P+LRfVUQsKiYQtM08rZO2tQYawZoqAyT7pyqAa44PqMNn96sTSF1MJB8vOF3anK3DdX52eMHiKREFypDaXExxTsvXMNl89RkNuk6KAT51ORw9NmugxOzSdpOk3Y2ST6lMp/OW907yS11tPTlbQxdjZ2lreniiB/GM1YsJyScKG6//XZyuYNr3pncIqBcHDfeeGPn63Q6zfve9z5e//rX85rXvAbXnRxY7Lou2Wy287MzPd7V1dURORzHmfH5Usppj7W/br/+yU4ijCQkJMwLMzkvNE3M+v2nmplyH/ZXXP7nyREANi0tsrI3i+NH3LNjjJ0jDd7ze2vpL6SouQFogtc8ZwkvWNvDtbdu5X+3jQLK/TxYdrl/zzi/96w+gM7OYNOPKGVMMlZy6T0ZmbQ7mTIwdQ1dE4SxxNTVP/VW9SkcWevB1NecSlL5mHCkHG1FaqqgUfMCYilxAtUKIkgqe48XbhDjBh5CQNYy6MmqG+66G1LzVOhoGynhgT1lAB7fXyVnG2Qsnayl4UayE8IaRjFOECMljDZ8fr1tlKxtsLw7gxtGjDd9Gl5ELCWPDlbYsKhIytTZMdxA0wTrFuQ6Yb3jDZ9HBiq4ofp3wQ1UKKyuC4SQmJpg92iTf/rBYwDzIo4c7XVwpmySlK1GhfaV3VmzSY6GC9b0sriYYvdok5SpTRqnkTKm6gTTKpYTEk4kuVxukjAyG5s3b+YHP/gBf/3Xf93ZjPN9H03T2LRpE1/96lcn/fzWrVtZt04F9K9bt44tW7ZMe/xFL3oRxWKR/v5+tm7d2hmnGR4eplwus379euI4plwuMzIyQm+v+u9k27ZtLFy4kHw+f8y//4kgqetNSEg4Zh4ZqPCRHz7GB7//KB/94eN88PuP8pEfPsZ/PTAw4/cfGag8pec7NfchlzLQBOyvOhi6QNfgQNVDFy3L8YIcQzWPm+4boCtjsaCQwjZU7eWCQoqPXPZsPvTKDfRkD6r3O0ebXPWNB/jUz7dQ90Ik4AYRQ1WX4Zo3qakm4eSgvTu5r+IgpSSfMiikDZp+iJQxTT+kmDbJ28YR1zVOfc2JJJWPCXNlrhWpQRTzm51jbN5XpemHjNQ9mq18jITji5Qq12V/1WVfxUUCCws2i0spVdU+5e+gPZoz0vAZbQboQv0dWi1xti2oCNSITdUNeHigwvbheqcGVwBDNY+7doyyd7zJhsVFVvVksQxNVeTWXDbvr+IEIYWUSdVVn01pSydlaMQS/EjSl7dw/Igv3L6dMDz2z6qjvQ5OzCZZXEoTS1oCECzuStOXt3lwT2Veqs8NQ+OdF64hbekMVT0cPySKYxw/ZKjqkbZ03nHh6nkdMUpIOB6USiVuvPFGrr/+esIwZHBwkE984hO8+tWv5g/+4A8YGRnhhhtuIAgC7rrrLm655ZZOrshrX/tabrnlFu666y6CIOCGG25gdHSUiy++GIDXvOY1fP7zn2fPnj3U63U+9rGP8bznPY/ly5ezcuVKzjnnHD72sY9Rr9fZs2cPn/vc53jta1/7VL4dc+IZs0U1OjrKP/zDP3DPPfeg6zqvfOUref/7349hPGPegoSE48JsjRt3bx/lBw8O0pe3WdP31AS+zcZMuQ81N+zkSQCTsiRmyoKwDZ1y06fmBsQIXrSuj+cs7+L6O3bw/QcHAbUj+/0HB7lz6wh/cdFaXri2lxhBzQ1wAhXQmozXnDzMlOWwoidLxVEL41xrh7buHXnrwbHkQyQkHAsNL2S04TFW9+nOWXRnLcYaPm4QJ26RE0wUSypOQMUJsA1N5ZGU0rz6nMXsHHX4+WND1L2DYySxhLITghOiC0HWNjA0QRhGNMOYIJadAPAwhhBJytAopk3cQDWh6Zrg7S9ayedu3cZdO8bQBPihpO6FWIZGxfEJI4mh0QrlFRiaGhkJY0EhbTJQcbhz2wgXnrbgmH7/ydfBGrrQlJAjBJGM6c7aM14HJ2aTZG2dmhsSRDGmrrUa6mbOJjla2u6Y627fxmDFpeqGGLpgRU+Wd1y4et5GixISjicLFy7kuuuu45Of/CSf//znsW2bSy65hPe9733Yts2Xv/xlPvrRj3LttdfS3d3NNddcw3nnnQeolpoPfvCDfOhDH+LAgQOsXbuWL37xi5RKJQCuvPJKwjDkiiuuoNFo8PznP59PfepTnWNfe+21fPjDH+aiiy5C0zRe9apX8e53v/speBeOjmeMKnCq9yonJJyMzNa4kbV1wkjV04aRJGerueKTpYljptyHqXkSE7MkYPoMtK4JenI2GUtnrBHghxE52+CvXrKOlzxLhbPuGlVhVqMNnw99/zEuWNPDVRetoy9vJ+M1JylTsxy8IGZxMYWXtbANjYoT4IbRnFoPjiYfIiHhWIhjyXfvHyBnG0SRpOlHZCyD7qxNuelPa09JOHGo6l+fsQY8Z2k3f3lRH1U34Bt37+bGu3ezZ3zyjH67ih5UzlHeNtCEysJoN960P0UlgBD4UcRDe8u891sPMN4IaPoRpi6wdDVE5YcRTqB+3tL1zme3ECBjFf5qGRpVN2S4Nj+pNBuXFDlzWZEv3bGDqhsSS4kmBIW0we+evmDG6+DEz2ohlFgzkeOR0XTZWUu4ZOMi7tw2wnDNoy9vc/6qHvZWHB7cUz6pRoITEmbjec97Ht/85jdnfOyMM86Y9TGAyy67jMsuu2zGx0zT5Oqrr+bqq6+e8fHe3l6uvfbauZ/wScIzYiW+a9euU75XOSHhZGS2xo1aK6m/kFZW3bbzAk6OJo6Z5p0n5kkAk7IkYPYZ6LRlsMjQKTcDqq5PLNUC8At/cg7fvHcPX79rVyd4785to/x2d5m3vmAVl521GF0TrfGaiKxtUsqYmHpi032qmSnLYXlXht3jzaPOypmvFqWEhCNh4k57EMVsH6lTdVS+RT5lkrd1yk5IIW3ghTEVJwn/PdFIoOwFCCEopi3+/MVreesLVvNvP9/MzfcNMNacHtoaxlI5SQBLV2HhQgiIYxpBxFjDx9AEpi7ww5jRuk/Ti0hbOhlLtavFsjWOg2obUikm6jokpfqM1oSY17DROJZ86Vc7uP6OHbh+SHfGxNAFYSRpeCE33LmTpV2ZaY6MpyqjyTC0jkvmkYEK//zfmyeFtK9dkOPy5yxNRO2EhKcZzwhhZMuWLXPqVfZ9H9/3O19P7Y1OSEhQzNa40XZfpG2duhdNcl7AU9/E0Z53fnhvhbW2crq08yTGGj5SSnpzKfK2ukS2Z6A3LS3NmAWhaYLunEXa0jruEVPX+JPzVvDi9X3828+f5IE9KlfFCSI+c9tWfv74Af7699ezpi9HLOmM1xRTBoVkvOYpZ6bGo2MV8eazkSkh4VBMvDbnNINzMl2Tqk4dP+LuHWNsWlIiiGL+d9sIwQmIPdIFRMkcT4fh6uT2hycO1Ng54rC6L8eKSFJ2Aw5UXepuRDQlm8OPYvxmqyGrVV8LAikkjqecJF4Q052zaLacJVlLp+oERLFE05Q447UqaGUsCWKJZejoImZ8nsJGHxmocNNv9vDt+/a2WncEXhRjmSZ5SyeXMhiqenzh9u1csnHRpAyPmT6r2xzuc3muzBQU/9i+6oyjwifDSHBCQsL884wQRmbqZD5Ur/J1113HZz7zmRN2fgkJpyqz7ea03RdeGE9zXsBT38QxW+7DwkKaoaqyDfcXbCIJjhcecRbEZPeIaoFY1p3hX193Jj959ADX3b6NaksM2ry/xp9//be8/rlL+dPzVmCbOmEUM9bwafgRXRmTdDJek5CQcBRMuzZPqUhteCo7IZJqVKPtGTjemsXTVRTRANPQ8OYYVDpcOyiMtEdTh2oeaxcoAdULIoIwoidrE0URo80Ax4+mvY9OGOO0a8RNnaxtkE2p15RSYmgagxWnFfgqkEjC1jRVDNS9CEFrlCaMGCiHZG2Dd1y4Gk0TbB+uH1XjXDuDbMdInSCKsQ0NXdPwgpgg8unOWtiGPmueycTP6i0HahTSFroGUQxVx6cnN3M2yVx5ZKAyaXzSNjXW9uVURs+UUeFcymCtleWRfVW+cPs23nPRWjQhaHhR4gRMSDjFeUasujOZzJx6ld/5znfylre8pfN1vV7nwgsvPL4nmZBwCjLbbk4+ZVBIGQyWHRaX0h3nBcz/Ls/RMlvuw4Xr+5DAeNNn50hjzlkQbfdIxtIZa/p4QQRC8LKNCzlvdTef/+U2fv74EKAC+b5xzx5uf3KY975kPees6Oq01xxIxmsSEhKOksPttNfckMXFFPsrTUbqPhLRul0+vjxda4LTpkbGNhiu+3P6HUcaQef/TxpNhY7Dp5SxefJADdvQKGUslhQ1nDBiqOrjziDEOEGEEyihI2vp2IaGISBl6PhhiKVrGJqmQlZnOVFD11jWnQHgIz98bNoYyVnLSty/a5zf7Bqn6avQ8ueu6OK1z13W+ZycmEHWnbHYMdJE1wSaUCJSEMbU3AA7px0yz2TjkiKXbFrEdbdvY9vIuAqM1QWLi2ku2bTomB0bswXI37trjH1ll2cvLk7672e84bN9pM5Yw2fHcIO7doxi6BpdGRVwnIzZJCScujwjhJF169bNqVfZsiwsy5r2/YSEhMkcqnHD0AVpS8fQBfXWjPPJ1sQxW+4DcMxZEClLZ6GRmuQe6cpY/N3Ln8XFG/r51M+3sK+idgsHyy7vu+khLt7Qz7suXE0pYyXjNQkJCUfNkbQhXXHecv7tZ0/S8EI0JNEJUC3SpkbzRMzsnGC8MKaYEXMWfgoTXJPt8SfPiHjiQLWTCaNrgkLKpOYFjI87WIbKFllcTCFRAkrVCfCn2EgkUPcj6n5LJLENenKp1iiLpNz0IYqJJZi6YHlXhkhCzjY4rT/Ho/uq/OtPn6QvZ7G4lJnUOHfTfXvxw5hYSuJYommCbcN17ts9zj+/ZhMblxQnCT01L0ATgiiWSKEkOE1TOSZBJAmjyXkmE90o+ysOP3hoHxnL4OxlJQwhCFvi3g8f2seavtysIsThXC2zBcjnUgaLCyl2jjTZV3FYXEohhGC84fPIQAU3jNCFIIhiXB90XSKkT0/WSsZsEhJOYZ4RwsjEXuUPf/jDjI+Pn3K9ygkJJyuzOS/OW93LmcuKPLCnfFI3ccyW+zAfWRAzuUckcO7Kbr70pufy1f/dyXfu29upXfzZYwe4e/so737xGi7e0I8QIhmvSUhIOCoO14aUsXR6sjYCGCg7nevQkXI0OoplPD2FkVBCLKUKPZ3DvNC6voObc/mUQRDFPLS3TBDHWIaOqQsk4IcRWUunlLZIWcptUXZ8immLtCnIpzLEcUzZCam74bQ8EgnUvZC6F6IJJcgsyNtEsWosKjsB20caKnhVE+web2K2xmE3Lil0RmWzts5Yw6fmhghUXphuCCIp8YKIx/fV+MR/P8FX3nzupJybrKVhGYKGHyGY/Pff9ALcIO7kmUwca3H9iIGKQxhJzlpaxDR0gigmo+ssyNtsG27M2nA343jMFDfHbAHyAJapkzI1xps+NVc5ex7bV6Xhh3RlTIZrHhIoZA42lh2oepy9rMjWQ5xXQkLCycszZoV9qvcqJySczByqcePSTYuf8U0cHfeIE1BzAiIpSZk677xwDRc9q59//emTPHGgBkDVDfmXnzzBTx87wHtfsp4lXelJ4zW5lEkxnYzXJCQkHJ5DXZsf3FPG1DU2LCpQcUKafjinm/q5iiKmRicE9OmGBlSaIYauEcXTM0Bme866/oMC/PKuDF4YUXGCjtNSSokQqma3GUlW96X42Gs2cv0dO7j9yWGqbkDdCwljn6ytk7dNurMWQRSpmt4gYopGQiyh7ISUnRC9Vf+7qJhGQxLGUPMC6m5AFKtRnHDCL1N1fEbqqpxAE6AJgRBgCIFuGzS9kN/sHGPbUH1Szo0fxeozS0ZMlcVGGwH5lMoz2XygNmmsJbRjdow28MKYO7eNkjJ1NCGUiyZtsLAwc8PdbOMxU90cswXIA+Rtg1LGZLDs8NDeMk4QU3EC9FZjjxfGpEwdS9cAQcYyqDgBdT96ypv3EhISjo5njDByqvcqJySc7MzmvEiaOBSaJujOWmQtndHGQffI2gU5PvNHZ/O9+wf48p07cFu7qb/dXeatX/sNf3reCl7/3KUYukYsoeoENP2IUtogn0rGaxISEg7NbNfg9o1r3VMtWsW02cobmcNrw7Qb3dmIJFhP01qaUtYkaxmMNvwjDrJNWTqNCULR7vEmUSwJpMTxYiakwuCFatwljGPyKZO/fMk6BisuSNgyVMN3Q6qO+mMbGjnboCdns0ATOH5I2Qk6ny0TiWJJ2QkoOwGmLujKmCwspIjimNFGgBfGTMxO31f1VKNN6+Sk6veF1u9sGRpuGPOrrcO86XdWsXZBjof2lKm5IVKCrsHUWJT2+7WyJzNtrGW07hFGkiiO8VuBKKW0iQTGGj51L6QrbU1quDvUeMxaO8fWoXrHzXHIOmChRph2RU1G6z4Z20BT38YN1BhR2tQ6r29oAieWBGFMMWM9pc17CQkJR0ey5ZiQkJBwArFNnUXFFKWMhd5aUOma4LXnLOUrbz6X81Z3d37WD2Ou/9UO/vzrv+WxwWrn+2EUM9rw2VdxcZ+mO7AJCQnHl3ZA63jTR9dUm5hlTBZaZ5NddaHyQrK2weJiiqx5+OVkLNWfp6NhMI4ly7vSLCul6cvbpGZ5PwRKHMrZOvmUSSF9sCmo4gQM1Txka6ZJTvjTPsZwzaPiBKzuzbFpaZGxhofbEtnbeKH6fNg91uRA1UUCi4ppVvZk6MlamLP8BQSRZKjms3W4wUDZxdQ1+gspLEMjZWpoQgkpbSEDIaYJ80IIkBLHj9g52uCMJUWkhH0VBzeMiKV6bvuPpQtMDRp+xL/8+HG2HKhNGmsx2jkkoVSvG0SMNHzGmwF+GFF3Q8Yd5ZZpc6jxGCHEJDdH+7+BfRVHiTwTkHHM3nGHfMpgSUk1WcaAlJC2NHQBThB3nhe28mDaddhPZfNeQkLC0ZEIIwkJCQknGCEEXVmLhcUUKVPv3Hz0F1J89FUb+eClG+jOHgyA3j7S4C++cT+f/sUWGp7agZJSjdfsrzqM1D3C6Ok3t5+QkHD8aAe0LiqmiFvXE1MTk8QQTUxfKAoOjlGkDI3FpTRdWZMjQZs61/E0IYgk3Vmb5b1ZQIkDGkpAMjQwhBIBSmklhuQsJSgVJwgjD+4Zp9wMphlq2iJCLKHqBZSbPpomOGtZifIMoasTafoRB6oeu8eajDV90pbO0u4My7rTFFLGrCKVH8mOuHLHllF2jjRImRqrejIUU4bKImGyyCWlJIhihAb37ynzwe8/yjfv2UPdDwkiiRvEtA0mhi7IWDopU8c2lajx+L4a5WZA2pow1iJUdksYy44zSdfUKI0XKgEmmGJB6YzHWNPHY0DloniBcnO0/xvozlpsHaqrfJZYUndDHtlXJYolGxeXOGdlN89f1c2iYoq0pdOXs9XrhGpMCCRNP6SYNslZOvsqDusW5J/S5r2EhIS5kwgjCQkJCU8RM7lHhBBcuL6PG958LpeeuajzsxL4rwcGecsN9/KrLSOd77fHa/ZVXKqOP23XKyEhIWE2Ni4p8pcvWc95q7sRQhC1xh0E6gY2lura074JTpsaC3K2CsGMJY0gYutwHb81MaAfxg2i6xoLiym6Mia28fSxjtiGIEaya7SBF4SYht66qT/o+ohiiRfGdKVNurImq/tyVJyA7cN1Htpb5qbfDsw4fjPRORJHqoUmjiUP7ClTSh+ZIBXFkqoTsnfcYbDs0PQiShmLFT1Z5fiZRUQAGG343PbEMDfcuZN7doyTsXSWdqVZWLTJWjqaUG4WP5JEkcTSdcabPqW0xcreLIWUSdAS7mXnF5rwm0oldvihJIxjnAkuyCCMp70nYso/VYPbwZGVieMxMzHVzdEOKT5jaZGy47NzpEHZ8Vndk2VhIcXComqkKaQtNiwqkrUMap6qKBZAtekz1vCxdY0FeYtHBqvomuD5q7pnPH5CQsLJS+LxSkhISHgKabtHMlOyR3Ipg/e+ZD0vOb2fT/7sSXaNNQEYqfv8n+8/ygvW9vIXv7e2U28YtMZr6l5Ed8YidYiFbkJCQkKbjUuKfOoNZ/Ozx/bzg4f2sW24wf6qQ72VC2HognzK5LxV3ZyxtMjPHx+i7gbkUgYyBqGpVhs4fHzIwmKKc1d0UXZCfrNzFC8Mjtvv1c6DOBGRJn4o2V92OVBxccMIpBpN8sMYTQgQ6lx0XXWyNPyI7cN1/vlHm7ENjdGG1wk1PRQxMFr32Dna4IHdZRw/mlPOC6hRGy/0qTg+tqGTSxksKKSQUtL0QppBhBtE07JAIgn7qqpiXgAZS6OUsejL2bhRTMMJcAUsLKRY25ej7kfsHmuwfbg+qfEoBuIYIj/qtO6YukATsLCYZl/FYa2tskH8SCIlmLrKJhGozzpNCGxTw9Q1wlhSdQ7+e9Qej3l4b6XzOm2klOyrOGxaWmJlT7ZT5xvFkiuevxyAhheRTxnEUvKPtzw2KX+kK2uxcUmR7SN1xho+pq6RsnQMXcM2dbaNNNRxgK/fvYu7doxOasFJSEg4uUmEkYSEhISTgLZ7pOIEVJpBp27xjKVFrvuTc/jWvXv4+t27CFqr/F9tHeG3u8d5+wtXcemZi9GEmDRek2+11xhJe01CQsJh0DTBH2xcxMUbFrJztEHFUSMbDT9CE7C+P8/K7iwf/fHj1NyAMJLsGnWIWrkKU90f7a+mahLjTZ+7doyxsJgmb+uMNI6fMJK2dHRNEMaqSvZ4CiReGLFtpE7Na4lJrcwW09CwdI0wigljieOH1F2dnpzF0q4MaUtnuOayZ9w5Ygt301fNNYMVhzCOsQyBG879l9MEOEHU+jv2yKdUoHc+ZRLFMXU3oBHE6JqG44eT3j8JNPyYhu8ihGpwWdWTppix6M5abBmuM1T1GKp5+GFMK3pkEjHgRSrIVQKltMEbnruM790/0MkIMTSQSMKW+UPTWg4mIchaOn4ksXVtUlZLezxmYNzpvE7aUq00+yoO3VmL1zxnCY/tq85a57u6L0ccyxkFlq6sxXPSJR7ZV2V1T5b3XLSWbcMNbrhzJw0/ZFV3lrRtzNiCMxNtceaZ3NyXkHCykAgjCQkJCScJQghKGYu0Odk9Yhkaf3L+Ci48rY9P/uxJHtpbAdQC+dO/2MrPHjvAX//+aazqVfPMsVRBfqq9xiTXmglPSEhImMqR3phtH67zwO4yo42AKI7JWAZGS3ioukcmcDTcgJobMlB2pt0ozze2oaEJgSYkpq5RcY6fCBPHylkjW+GykYQ4jDE0QXfWBAQNL6DqhhTSJpuWljrXZEvXsHUN7whyogTw4J4yGxYV8MKYjKkjiJVLZY6E8UGxQn1mhFRarTYLchZLuzP4seT0/jyxhD1jDgNlh6GaO8kBIqWqmX9woIalCwppk56sRX9BuRkbXkjDj/CmWlA6L6DeP13TWNqV5qqL1nUEi33lyceKW2M4YRThVpSzY1lfblJWCxwcj2m/zoGqEj42LS3xmucsATiiOt9DCSyLi2neceEa1vbl+cY9e1p5JMVDtuBM/e/qkYHKrOJM4jJJSDjxJMJIQkJCwknGJPeIExC1VobLuzN88vVn8uOH93Pd/2yn3gpifWxfjXf8x3288dxl/PHzl3fC7IIoZqThUfPCZLwmISFhGnO5Mas4AYNlh0hKChOqwk1dqBreCcymeYSxyjCJpWy5OY7Hb6XwgpCefBpDE7iBajCJjkCN0VrZIHNB0wSO33JGtF4jiiVSqGYVIQRRLBEI+gv2JKHaNDTl7BNQO8xxDA2Gqh4NP8LWNfwoRtN0YO7CyMTfURfq7zGWqvWs7oWMNHyWlNLYhkYxbdGbs1nfn2Nw3OG+3eM4M7hw/EgyUvcZqfvoAlKmTiljUcxYhHFMw4toeOEkkUQChZROd9bke/cPcs0lz2LDJRvYPlLnUz/fwnDNpeFHSCk7GSUCkAKaQcSyrjTLuzJsH65PEvc2LimyYVFhmugH8JEfPqbqfPuy1P2IStPHNDTW9mXZOtzoCBmHE1g2Limyfbh+xC04E2uzHxmoHJE4kzhKEhJOHIkwkpCQkHASMtE9Mtb0cf2oYyG+ZNMizl/Tw+d+uY1bNw8BahF+4927+eUTw7z3Jet4zoougGS8JiEhYUaO9MasTdUJ8KKWS2GmmlbUDWva1AnimDCSKldDU6KAlFDKmKRNld/gRzF+3Z9VROm8NmqEIo5nF1xmImsbmK1rXdrSsQ1BMzj8KxhC4M/ZziJb7STqmhu12leEEDRbbgkZS3QNcvbkpXfeNiikDYaqrrrhn+UIqg1IEEk1frK4lGaw4tL0wkM+70jQBBi6rsQHGVF1Q2IJI3WP254YoZQx2LCoyIK8xWAFVvRkaPoR/fkUNS9koOJQbk525ERSZak0fKcV3KtTypgU02nCOKbpRdS9sJMZ0ptL8eSBKndsGaaUsSg3fcYbHrmUgRNECCHQJ3x2BZFqudlfdfnojx5n6/B0cW/DosK037Vd55uxdH67p0zVCTsjYYW0wcJCii1DNbaP1NX7PUP+yERxotOCU5y9BedANZ4UEBvHkpt/u1eJMwtys7pMpJR8tzValDhKEhKOP4kwkpCQkHASY5s6CwvKPVJ1QsJYLb67sxbXXPIsfn9DP5/6+Rb2t0LxBsoOV9/0EH/w7H7+/EVrKGaUxTgZr0lISAB1U7Z9pM7/e/s2BssOz15cQNPUDeeh7P+FtIltaHhhRMrUCCLl/NCEUK6I1uuXMgaRhPFGgNlykjQjlVWSsQwsQ29Vu0q6cxajhwkdVY0uMNer1eTYDUHGNmgG6ljKVCdaoySSOD4YYCrnKDHoAkxDJ4oi2re+hqZEkTCKKTcDNE0Qtypnn9hfY/1CcbCSXQhW9+YYPYRIpFwnIDSBqWtkLJ0lXWmqboCtc7DGfU5nfvBcbVMnimP8MCZuNRNpgKkJTF0wUve5Z8cYq3qz7Bptkk8ZdGct1izIIjTB6V6eXaMNHtxbIZjBbhN3RJKD/x4UMwaFdBpkjBPE7Bpp4IQR//bzLVgtN8yesSYSSXfWoumHeKFESoloVUXHEjbvr9EMIlb15CaJe48PVllQSDHe9CeJCpuWFBlr+JQbvhL6JoyEjTV8Gm6Ibel8+udbKDvBjPkjE5nYgtMOaZ3I1BYcOCjOHMplcv/ucZ44UMUL4iMSLhMSEo6dRBhJSEhIOMnpuEcsnbGGjxtEndnw563q5ktvfi433LmTm3+7t2OP/u9HD3DX9jHe9eI1XPysBZ3FVzJek5DwzKU9OvPw3gpPHqhhGeoGdFVvrnOjPpv9v5g2WVxMs3u0wWDFnXwXLsDQlauj6UdkLAO9FZQZtPIvUqbecXCErR360xfkeWSgTMUNYUK1LUC6FSra/vpYI0kmjvuoU1KOFtVwInADVQ0btUc1jvB1VeyFxDQMUq1A06xtAJK6G2HoSsxImxogGK55+FGZjUtKnfe8lDEppE3qXoDf+p0Fym1jaO0qdyUwjDcDrr9jB+PNgHIzQBdQSBvUvRCBQErJ4bJY27+fQL1m2tQJophYSiWMCIEfxYw7IZlIUkgZuGHE7rEmTT+gGYR0Zy2cIMLQBGlLZ01fjrGmz2DZxT3EjFQsoe6F1L0QXUBXxiRr6ei6oMs0WdGdJpcyGSw7bDkQ4YcxpYwa3RJI1ECSOv8wjpFoLC6kOqJELmXQE1rcvWOMHSMNnreym3TxYBjqk/trDFVdwli23v+DI2HFtMlQzSNo+GRMnVW9ucMKEnNpwWlzOJdJytQYLKug2DOXleaUW5KQkHD0JH7qhISEhFME21Duke6MhaEdvHynTZ13vXgNn7viOaxbcHA3q+IE/MuPN/M3Nz/cqdOEyeM1o3WP8AhC/xISEk5t2qMzD++tkDZVW0raVGLrIwMVxhoHnRtpS8cLJtv/V/ZkWdadxmsFS7QFgfY9uKmrUQmztdsvpcQPIzRNw9AFOdsgiCRuEFJzAwopg6Vdac5YWmJhIcXqBVnyKYOMpbF+QZbTFk4fg5gLURS3xi0kDS9kdMLvJ1FCg6ULLEMjiCSGLshYOrahzcmdogslJIRRTBBLbFMna2nUPSUIZS2dBfkUm5Z2sWlpiWLapNIMePJATeV5uCFbh+osLqXYsKjIsxblSJsauqYqbNV7K9CEaDl0oCtj8+zFRZ69uACtRjLT0EGApisxxZhhha+1fuf2vbvkoJvQ1JUDQwiB1hJkdE0JXaMNH10Iqm6AE8Sdz5D/3TbGbU+McNsTw2wdVuMpi0pplnWl6c6Y6Id5IyMJI42AXeMuO0cb1H3lMooiyZJSmvX9OUxdU+OkgQqztQ11bk4QE0RS/R2aBwUGKSU7Rhud9062xrlyKYO1C3Id5yRSTm/KidW/s5GULO9Ok0sZk5471vD57m8HiCe4YtotON1Zi61DdZVlE8vO32u7BWeigDHRZTITI3XlZllYTB02tyQhIWH+SBwjCQkJCacQQgiKLafHVPfI+v48n7viOXz3/gG+8qsdnV27+3aN89av/oY3nb+C152ztJMxkozXJCQ8M5iaaVD3QgxdQwhBIWVSdQN2jtTpynQhhJjR/q8QrZtTHdvQOzWsXhgRxbBxSYlVPRkeGawy1vAZa/hYukACFTdAyqCT5+CFMWMNn6Yf8QfPXsiL1vfy6V9soStjsyBv8evto0f9+6YMwYJCipobUQ0ilVFB23EAhlBxpX4kMZFkTI0glqzsyfD/vH4Td28f576dY9y7c4zhms9s0rGpCyWKtH4n5boQpEyLrGWwqjfDkq4MedugrUacsbTE5v1Vxps+m/fXKGVUS82rz17Md+8f4OG9FV64rpcnD6gwUSV6CKpOQN42OHdFF6IljC/pyrC4mOKRfVW6MxbdWZPN+2rsHndImxppQ+NAzSeWEqvV0uOFStjQtYMjSrqQlB2fMIKUob6ftgzytkHV9XH8mIobYGqCYsak6oSI1khMewxl11iTuhuCgLytHDBdGYuGH1JzVTPNIf8dlTBQ9vju/YOYuqDUbrcppoilJIoi/BiaXkQYxxgCVGSMIDfB+VhzQ6pOSNY2cIOYYIJ7RQhBV8ZkByr0tuoGLXeTErYqTkAsaVUsT1ZNDhWkeiQhrRM5nMtkf8XBNnT6cvaM71Xa0tlfjbj9iSF+u2ucvrzNBWt6MWZSwxISEo6YRBhJSEhIOAVpu0eqTkDFDTuuD10TvO6cpbxwbS+f+sUW7tkxBoAfxnzxjh38YvMQf33xep41IZSuPV5T99V4jW0m4zUJCU8npmYatAM/xxo+xbRJxjKoOGGn+WIm+//O0QbjTZ9NS4vsr7oqtDJSgkBvLkV/wSaWkj8+fwWaENTckP0Vlx88NMD/PDlCEEqEkKQMnaxtUHMD7t4xxukL81x+zlKiWGLpOn15m7oXUj2Get2cbZK3DboyFjtHm4RR3GrDEa2RDDBbzTGGJjANjYymYRs6KcPgLRes4i0XrML3I/70K/fw6L4qujg4BgJ0Mi50XaALCCKpGsSEYGlXFtv0WNdfQJ8y6tCVtXjeym4276/xp+ev4DkrujphnkIIBsYdxho+GxcXiaRyHuyvOgRhzMYlxY4o0kZoGqt6cpQdn/f83joGxh3+7edb6MqYZCydu7aP0fCVwOK3xmXaI0RxSyjywoMZK04o0YUklTaxTZ0+M4XjRzT9iCWlFP35FL/dU0ZOsVtIKdGExAkkbuAx0oCcZZBPGSwopIhi5dypeyFxHBMzc6BuLMELJQdqPgdqPppQQbrFtEnB1iillPOoGUSMN3zV5uNFFNLqfQmimCiWSrRq/d1O+ncjZaJrgiWlDA1fiShuELXyVSRxLAmI2TJUR2jawSwYZg5SbTNbC85Moy5tl8lsVcDdWYuUqeMEMbkZwtK3DtV4Yn+NzftrSKmqohcXU7zzwjVcdtaSaT+fkJBwZCTCSEJCQsIpyqHcIwuLKf751Rv55RPDfOa2rYy3GgO2Dzd4z/+9n1edvYS3vmAlGUt9DEipQuL2BS75lEEpY01b0CckJJyaTMs0aAV+Nr0KFScgbeqEcUzVCThQdWe0/7dfY2VvliWlNDUvJAhjTEMjb6vA1Z0jDRpexJnLSgCcsURy1/ZRlvVkyFk6B6oeTtC+cVU3fP2FVOeGsj1e4IeqyaXtSDmSzA+BukFMGRpZ26DihgzXPBpeiKaJ/4+9+46Tor7/B/6auvUa1zialDtARKQoKKCoiEaNkiCJ+lWjJhpjEBMTNdVowjfR9ESjRk3UWL6JsRA1kl9UsIUoKKigcHBHL8f1sn3a5/fH7C63V+BoV1/Px+MSd2d3bnZumdl577vAIynwaApyvBpCyV4egNsEtihLT5dZtL7o3d0cg6bKOLOsEEIC6kIJbKhqgeW4F9CppqrZfh1x00bQ4x47F548DM+8v6vzhpymgxy/hqnH5R0w8yDV+HP84BxsrQtjcI6vw9eeumCPJGycXlaINzfXYv3uZhRleZAf1GG3CFi2kyxxcgPourJ/WlDbjJh0eY3qBos8moyWuIWibB9M20kHxxoiBqxkcCk/6IHt6NhSG3FLkQQQMSxEDStZpqQi6FWR7dNgWo7bq8Rwp98cKJnEEe57LxS3oEpu4/GiHC+G5/uQMBxUhxKQWr07NEWGIrsBrKIsr5ut04oquaUxpu1gyvBc7GmKY9O+FkiSDK8qozacgKrIaIqZWLujAeMGZ2NYnu8gmVQuWZbaNWftzISSbFwydRheXrcXe5tjkAF4dQWThuXic1OGYGkye6htRsn2ujDW7W6GBIGCoAdeTUHCcrCzPor//ecGAGBwhOgwMTBCRNTHpbJHQnETTbH92SOSJOGs8UU4eWQeHnp7K5at3wfAvcBY+uEe/KeiDjfPLcWs0oL0uhwh9pfX+DVkebWeeElEdBR1NDkjL6Bj4lD3grshYsCwHERNGyd1kv7fdh1tjw2xhNXx9I3aMEblBxH0qhhVEMwIqEgCaIga2F4fySgv8HsU2I47jcWwO58Tk7pcVGUJqiIhx6fDsGw0Ro10j4qKmjD8moK45SBq2LAc94LStAVs252IUlYUgEdTELfsjO3fcPdIgQABAABJREFUH1BSIUOgRghIADyqnJziImA5AobloDDoQbZPg+U4CHoUjCkM4JM9LV1uyJkycWgOxhdnYeWWOtSGEijM8mBIjg8/eWVDlyaftM5G2FIbweBsH8IJC6G4u+2a4gYGUqUtmgyoipwusUkFowzbQXPUQJ5fR1PMhCwBZcVBfLijEXl+vd02CCEQilvJscLJMc1uIxokLIGEZaIx6gbh3ACJjmyfjqhhIxy3EDXt9N+0s7+3JYCasIGIYUOTgwjoKnyam2kxKOg2uTVMG44jYNkCeX4NLcnsDUlyG9NWtcRxynGDEDXcHiANEXcaUNDjjgXWFBmW7cCy3fKwD7Y3YF+zF2MKs1AfMTr9uwFuyVpXMkZSTZBTwS8J7pcZn51UgnkTBkOW3Z4ybTNKogkLHyWzdYpzvPBq7nvBp8vwajJqWhJ4+K2tuHBiCctqiA4DAyNERP2AJEnI9unwaioaogZiydRpAMjyarj13HGYN6EYv3l1M3Y1uo1Ya8MJ3PHipzijrAA3nV2Kglb1zKbtoC6cQDjB8hqivq6zngZ5AR1Tfbn4pKoFo/MDuPmcMowuCHZ4MXdUpm9IUkZAxXYEqkMJhOJWxgX9zmTzzNTo2FR/6I4umhVZgt+jIMujIm65DVAlAKMKA5AlCbsb49BU2S1VMG00xQz4dW86Y0UXgKrKHW5/KhhUWR3C1roIooYFK5lloSqSO01FljGywI9QzG2o6tNV/Pmd7cgL6FBlqdNSibYZOSltL5o9mowxhQHk+XXsbYp1ad+3zTzJ8+mwbIGE5SSby7rPV2UJerJXjGkLOELAo7qBATs5Yciw4u7kGl3ByopaVLcksFWEIclSRo+RurCBcNyCLCHdx8qjSLBtB3arfh2xZM+XesmdspLl0VCc44XluBkvobgJ0z5wflDEsPHhrmZIAAqzPMj1a9hWG4GmuI1+y4qD2NcSx7b6CLbUhpHr11GS40PUcKfpXH/GaADAoyu3YUtdBDIkmLZAQFdhWA4SppVu0JuwHOxsiGFfi4GJQ7IP6e+WGvHbOsiYaoLcEDEyxvBWNcfw/No9GJrnx8ShOR32LYkaFmwhkqU2mZdwkiQj26dhT3MMK7fUYc64ogPuQyJqj4ERIqJ+RFflZO8RIyN7BABOGpaLR750Mv5v9U7836qdsJKd9d+uqMOaHY247vTRuOikEsjJD92p8pp9ZhxBltcQ9VkH62kwJMeHr84Zg9KirMNex8Gmbxws0wHYf0H/6Mpt2NMURzzZNFWRBWRI6QanqetmRXZLKzyqjJhpu8EUR6Ao24tsrwYJyOilkuvT0Bh1MxdSGQJZXg3VzXHkBz3ttn9kfgAyJKzb0wzAPb4qshtgMG2B5pgbON7dEEMkYSHoUTFpaA48moK9TTG390OuF41R46ANOYH9F8314QSyfTo8Phm2A6zf3Zy+6O/qvm/b88Kny3jwzS0orwohL6Bjw94WxAy3iWoqywOyO+FGV2UYltszJdVYVgZQHUqgKWbCEQKDsz3JxrsOZEmCT5PRHHPLanyaAkcImMlJPe3eSwAggJaY2+PDo8oIelQEk71EEqadHunbwdPTBICaUAI1oQT8uoLSoiASpo0cn4aywiyEDRPhhI3mqIkd9RFMGZGL604fnd73V516HCqrwyjO8kDXZKzf04y46Y5YFkLAdvZP7jFsBx5VwYSS9tOSOgt2tB3x27YJ8sHG8Lb9G/63sg7b3ogg6On48k1X3ZKn2lCi851GRJ1iYISIqB9KZY80Rg1EW2WP6KqMa2aOxFnjCvGb1zZj/Z4WAO43cL9fXoHXN1bjW/PGYlTB/m9NbSHQEjMRS37gZHkNUd9zqJMzjsY6DifLZOLQHPxiwSR8R6xzm3w6DkIJO/ltebKHRHLUqq6600MkOMjyaIAEmJbIuOhs20vFpynwam4PCVVyp5+cNDyvw+13HIGdjRE3Q0R2G5ZKyeBDwnLgCKAxZsCvKxiS68PowiDyks06Uxe6+QEPbp5bhkjCPmB5ReqieXdjFJYtsKcpnp7gk+1V3UyIoix3LGxt1/5+bXtefGX2aNy7vAJ7m2Lu/kk2oHWSPV+CHhVxy0bcdEuOBABdljAo4IFPkxFJWHAcAQdAVXMCeqvRxg4EFEmCR5Xh0WTYjoBXk2FFTThtMkDaxjoSloOEZaA+4u7LoEfFoICOQYFkqU3CckfsHkDUsLFutxvA8qgyhuT4MDTPg5IcL0bk+RA2bByX78fxg/cH/3J8GnL8Gry6Ckc4qAsZgCTBo7iNcC3HDQ7l+TUYloOK2hC21oUzAoiHEuxo2wS5tc6m3rT+GzZFDWiqW/bk02UIIdKZPrIkwbLdoE5hVsfTbIjowBgYISLqp3RVRnG2F6GYicaYmZE9clx+AL+9dDKWrd+Hh97egkjC/dD56d4W3PDkGlw2fTiunHEc9GSdsoA72aYunEDEsJGXnFhARH3HoUzOOBrrOJwsE8Atbbl29iiEkt/CjypUIUsSwnELLXETg4I6Fk4div9U1GNLXQSOI5Dj01CY7cHGqhbEDdsdb+tV2/VScYTAkBw/Rhb4Mbu0ACcNz+10+1duqUNj1EReQINpuz1FhBDuZB+vCiGASMLC6IIgThiSnR7JC+y/0K2sDUOWpHRD2s5sr4/go51NqI+YsB0no0ylIWpAkWV4GqJYPLc0PfXnUP9+qcDWc2t2oeHTaoTjgO3YCOgqsnwaPKqCgFBQFzZgWBZUGRia64NpCzRETbfBd3JdAoDtOFAkCUhO+lEUd3tH5fvREDVRHzbgtEn5kNr8d9sgSTQ5AUduXWqTnGoTTlgIxy0YtgMZQJZPRbZXQ20okR5PD7iBlm31EWyrj8CrySgIepAf0LF2RyPe39GA0qIs+HQFI/L8mDg0G2u2N0KTJZiOAz1ZBmQ7bsNaXZXh1xVoiozmmInN1ZmBkUMJdrQrLWvjQFNvAGDWmAIMyfFiZ30UkgSEE+40HZGcNGTZDoYPCmDWmIIOn09EB8bACBFRP5fl09KTa2LG/tRkWZLw2UklmDkmH39YUYk3N9cCACxH4Kn3duLNTbW45ZwyTBmRl16XEEA0YSFhuCnoOX6N5TVEfcihTM44Gus43EyV9hNa3LKb04YWpJ83f/Kw9AXnvuY43t1Sh+aYiV0NMQQ9CrJ9GkYVBDHoEHqptFYbSrhNPH0aZNntQ5GaRqMpEmKGW+7hVeWMoEjKwS50W2uOmdjbFIMthFsGlFyfpkjI9mpoibvLQ3Er45h8qFKBrdNG5+O+FZXY0RB1RwUDMCwbkYTbR0WWgPyAJxkUcQMcspQZyJAkt+GtV1egykgGVByEEzbGFWWhKcvEut1NCMWtjMk3qV2VymTsKEDitCq10RUZQe/+UhvDchBKuBOEdEXGzNJ8lFeFsK8l3q70Jm462N0Yw+7GGJRko9kbzhiN4hwvFFnGmWOL0BQxsbUujGyvBsOyEDdTo5cByRaoj5ju39jd6owmq3uaYogbdpeCHYdaWtaWqsq4Yc4Y3PniJ9jXHE++D93tMiwHkiQh4FFQXh3qUgYYubraNJf6PwZGiIgGAE1JZo/ETTRFTZitskcGBXT86KIJOHdrPX73egVqkvXJuxtj+Paz6/CZEwbjhjmjkeNr1TRRCDTHDERNC7l+DUEPy2uIqGOHm6lysOelAjSpxpcNEQOlhUFsqQkjatqoC7kNpEsLsxA1rC71UmmtMMsDVZGSpQsqNEUCWl3/ugEECVInA0AOdqHbWkvMRMJ24NeUDjMPdEVG1HQzYY6ULEs4b2IJhub58fDbW/HBjgY0J9cb9KoYUxjAhqoQNEVCKFk+oykSnLajkwWQsB0EZRVSchJNllfF2OIs1ITiaI5ZyZG/EmJWm3Ia4a4n1d/EPkCQxLAdNEQMNCRLbbK9KgoDOgb53VKbfU0xDB/kQ8KyIQEIpTIp2qzHdoA3NtXijU21mDQsB2ePL8KcsYWYe3wxrE8E6sIJOMINvBimBcN2AyQJ00Y04Z7rJEnCklc2pJusOkJgb3MMPl3F0Lz245RbvwcOp7SsrYsmDcELa3dj9baG9EQkWZaQ7dMwfnAWTFtk9CmhA+tq01waGBgYISIaQLK8Grya4vYeadPY7tTR+Xjsmlw89t9teGHtnvSy//fpPry3tR6LzhqDs8cXpT/MpctrQgmEEyyvIaLOHW6mysGe11GPB7+uYmtdGM1REy0xC5W1IZw3YTAumXZoFzutSxe8mgypVQRECHeKSrZPhWXvL7HZv7xrF7op2T4NHlVGwrLh0xVkFp0IJCwbHtXNgjlaJg7Nwe8unYytdWFsrg4BkDC2OAhHCFzz2PsIJctWVFlKj7tNjfMF3NIZw3KnzJjJoE5uQMes0nz8t7Ie4UQEeX4dCdMGLDv5SjIzRRTZXaGUvO33KHCEOxmto+k08eTvaoy6o+qzvBqG5voxfJAPcdNxm95KgCW55aS6KiNq2O3WtW53M9btbsZ9Kypxysg8zCkrxNubaxA2bGR7VeRneaHIgGEJtMQMtMRs6KqM59fsRlPU3N9kNWFhZ0MU63Y3wafJGNRqulvb98Dhlpa1tr0+AkcAc8cXI5ywEDdteDUFg7O9kGS33KxtnxLqWEdNc6MJC+9va8CGvS348qxRmDehmAGmAYSBESKiAUZTZBRleRHWTTRGMrNHfLqCr59Zirnji/Hr1zajsiYMAGiKmfjpsnL8+9NqfPOcMgzJ3f/NmMPyGqJe62Bp4n09jbyjHg95AR3T/HkIJSw0Rw3ETAdXnDqiy5kiKanShf/95wbUtCSQ7dOSE1sctMRM+HUF18waiY93NR/2hW5Kjk/DkBwfqppiaI6ZGT1GooYFTZZRkuPNyNw7GmRZQmlRVruGoicfl4cV5TUwbQFZldLZF+lSTACW5U4IiiYsFAZ1eHUVLXETf/7PNvg0BcPz/IhbNppjJkKJzOapEpKNdGVAU5RkM1sBwxbQZQleVYbj2GgbG0lNTbMdoCVuoSVuoSCo4bjCAE4YkgNVllATiiNqumVBuiJD9rilQh5VQUPUQKJVPxLbEXhvawPe29oACe4Y5tpwAnXhBPy6ioBHQZbP7XMCuD1Qxg/OSm9X0KdhyvBcrNrWgA93NWH6yEHwe9RO3wNH2gQ51afEn+P2hWnrUMq3BrKOAqoNEQPb6sJoiZnYXh/Fj//5Kd7bWn/IAdW+fkwdyBgYISIaoIIeDd7kB8W22SPjBmfhwSum4vm1u/H4yu3pxnYf7GjEV/7yAa6eORJfmDYsIwDC8hqi3uVgaeL9IY2804aWkoQsrwa/rmJ7XSTdYPpQzZ88FADw0FtbsLc5jpa4BVWRcFx+AF+dMxrzJw/N2I+HM+0HcCf4TB6Ri4TlZkSE4jZiyak0g/w6VEXClBF5Xco+OVKyLOGrZ4zBjvooNla1wLQdyI4Ey9kfUHCwP0gSM23sbIylR8BLEpDlUWHaAvXJhrdBXUHUsNP9RdzMHhmFWe7Y36hhwxECTTETthAQjhsEscX+E1Mqq0QIwGl1v0dV0n/foFdFbQgo8OvITfZqsR13xLJhOfBpcnKMs4amqAmj1RcDAkhnlkgAoqYNW7gTe3x+D6qaYxgxyI+ArkKS3f1hWA4GBT2YNCwHlbVhVLXE3ek8B3gPHEkT5CPtU0KutgHVhoiBT/Y0I2HZ8Osq8hR3BPj7Oxqwp2n/yOWD6S3H1Pr6etxxxx1YvXo1FEXBxRdfjO985ztQVb4vDoR7h4hoAFMPkD2iyBK+ePJwnF5WgN+/XoHV2xsBuB3/H357K1ZsrMG3zi3D+MHZ6ee0K6/xu5MOiKh7dZQmHjNsrN/djD2NMVw4qQSvrKvqdHlXLwR6WndcKM6fPBQXTizByi11qA0lUJjlwawxBVCTDTmPxrSf1mUWDREDw/JUKMnAQChuHVL2ydEwcWgOlnxuIr7z/DrsrI/CEQK201kPkP3BBFUGFEVGOGHj073N8OkqirI88GoKbJGA5Qjk+lQYNhD0uFNl8oMeeDUZlTVhyABiljsNRoFAVYuR/n2SlAyIJKfgAG5z2lGtgkVZXg2GDZi2Ba+mYGxxAI0RE1HDHUEcipuwHTOZTSJhZH4QUdNGVbM7HjklFSQxbRuWnUDcdNAcM9EcM6EqcXhUBT5dgU9V4Ncl+LQsCAFcPmM4hub6D/oeONzSsqPRp4QyA6pCCGyrCyNh2enGx0IIxE0HQ7K9aIgYXerbcrBjbnceU7/5zW+iuLgY77zzDurq6nDjjTfi8ccfx3XXXdctv7+v6qRdFBERDSRBj4aSHC+yvBranvdLcny4e8GJ+MEFxyO3VepuZW0YN/3fh7j/jUrEjMxvY1PlNfua4u6IzLajAojomGmbJh70qlBkCUGvitKiIBoiBh56awvqw4lOl7+wdk+f+HebulCsao5BiMztTV0olhVlHfGFoqrKmDOuCAtPHo4544rSQZGU1IXuScNzMbrw4BNvOpIqszhxWA4sR6Al7k6ImTQst0cCVZOG5eIXl0zCpGHZcIQ7lUaR3GapHb06RXYzSTRFhp5s1powbQACHlXBoIAHqizBERIUSULYsDCyIIib55bhq2eMwbA8P3IDulsuJIC45QY+gFTQRYKuSlCSfU5kCSjK8kBqta+zPCqyvApCycyebbVR7G6MwaPKyA/qKAjqKM72Ic+vQVPcSUMnDcvBvOOLUBjUoXTw2uKWg+rkSOA1O5rw6d4W7GuJozaUwL6WOBoiCYTiJvKDOsqKso7oPXAwqQDaoICOypowwnHLHWUct1BZE+72AFpf1TqgGoq704/8upoONFnJbC1dUzJGLnemK8fc7jqm7tixA6tXr8Ztt90Gn8+H4cOH4+tf/zqefvrpY/67+zpmjBAREQA3e6QwywOfLrfLHpEkCXOPL8IpI/Pw8NtbseyTfQDcAMjza/fgnYo6fGNuGU4bk5+xTlsINEcNRA2W1xB1l476bqRIkjs5pLI2jCnD8zpc3vpCoLc3cDwaDS17k6ORfXI0nTgsF+dPHII1O5shy25PjlQvDmB/tggAKLIM03aSo25TvUAEYqYDvy5Dkd3xvuMHZyV7gSRw5akj0gGfdO+N6jCaYyZkWcKYwgD8uoxX1u1zA0WWOwkoN6Dhs5NKsK850e7vrikyfLrbxLU5ZsCnKagNu+ehLI8Kjypj+CC/G3xKltPoioLxg7OxsaoZUcPG4BwfQnETdWEjI0MmatqoqAmjoibs9oXJ9aIkx4ualgQmDMmBIgO7G6Pwayp8ugxvB1OGjtSR9imhzMybPL8G2xFQ0//G3L4++QEPsjwqbIGD9m052DH3aBxTw+Fwxm1d16HrervHVVRUIDc3F8XFxen7xowZg71796KlpQXZ2dntnkMuBkaIiChDqvdIU8xEOG5m9B7J9mm49bxxOGdCMX7z2mbsbowBAGpCCfzgH5/gzLGFuOnsUgwK7D9Zty6viSRs5LK8huiY6rTvRpIiSbBsAaWTvOG+1sCxv10oHm6ZxbHi1xXoigy/LqMpZkGTJSiylGxi2qYHiARYtjveNzXBxrYFWl9sluR4EU7YyPE7Gc1kDxQUuuOCE7D0oz3Y0xTD0FwfPj95KHRd6bC/y6mjC3DS8By8tqEab2+uBYQb+M8PeNKZFpYDBD0KPJqMfL8HXl2BX3fLKjbua4GuShic40NJrg+yJCEUN7GlNjNjIFVas7EqhByviinD85LbLcGwDLTE3WCRX0+W3RzFIElvC6D1Na0DqnubYhAQMG0HkiQhaljwqgpGFQQASUIsYR20HO9gx9yjcUydM2dOxu2bbroJixcvbve4SCQCny9zdHTqdjQaZWDkABgYISKidlRFRkGy7rspasJo1cUfACYPz8WfvnQynl61A39dvSvddO/NzbV4f0cDbjhjNC44sSQ9QQBws0siyfGC2V4NOT6NH+KIjoGD9d2whYCqSLCdDp6MvtnAkReKx05hliedIaJIUvriXu7gIl+VZciSW4qQ6kcihNufI3WxKYBOe2F0FhTSdQWXTh/R7v4D/d1PGJKNfc1x+DQZOX4dWR4VLXELiizBsB00RN3mqWOKbGQpGgqCOjyqDL+u4rwTiuHzqAjoCobmucGRfc1xPPvBbqzYVI3mWOYFbnPcwpOrduCv7+/EjFGDcPb4Ipw2Jh9eDWiOOQjFzXSQxKsr8B+FIElvC6D1NamA6nNrduHVDdVojJoI6AryAx6MKgggL6B3uW9Ld/Q6euuttxAM7v97d5QtAgB+vx+xWCzz9ydvBwLsPXMgfeeMdxC7d+/GPffcgw8++ABCCEybNg3f+973MHz4cADAtm3bcNddd2HdunUIBAK48sor8bWvfa2Ht5qIqHc7UPaIrsq4dtYonDW+CL95dTM+2dsCAIgkbPzmtQq8tqEat8wb2+7DhO0INEUNRA0bOX6V5TVER9nBGjSG4haG5HjREjNQnO3pNw0ceaF4bMwaU4AhOV7srI9ClQHTTjZHlfc3Y039v1dT3ABEzIBpuxkkMctBYdCL0qIgNEU+6r0wOvu7jy4I4sRhOVi/uxlDPWpyUpGKbJ+KhogBIQQKgl54VQXhhAUIgT1NMUwZnot5JxQjajqIGzaEEBAABud4sXhuKRadPQarttbj7Yo6rN3RhNpwIv07LUdg5ZZ6rNxSD5+mYFZpPuYeX4RpI/KgKm6QpKV1kEST4ddUBvB6SCqwdtrofDy6cjsihoVRgwLweVSE41aXy/G6oyluMBjMCIx0pqysDE1NTairq0NBQQEAYMuWLRg8eDCysg5tZPlA02+ary5atAg5OTlYsWIFVqxYgdzcXHz9618HAJimia997Ws48cQTsWrVKjz88MN4+umn8a9//auHt5qIqPdLZY8UZnuhq+1PGyPzA/jdZZNxyzllCOj700jX72nBV59Yg8dXbm+XcSIAJCwbdaEEqlvi7ZYT0eHrSoPGG+aMQX7QwwaOdFCqKuOGOWPg0xVYjoCAQNy001kh+wkEdMWdYCOAXJ+G/5k+HOdNGIxheT40x0w0xYxuaybb0b8DRwCDs32wbHfKTnG2B7YAwnELFTVheDUF804YjGyfjsHZXgzJ9SHXr8OjKunG5LIk4bQxBfjOZ8bjb1+dgT9cPgWfmzwEef7MIH/MtPH6xhp874VP8IWH3sPvXq/A+t3NsB0By3bQEjNRG0pgd1MMNaE4wgkzYzIOdQ9ZlnDexBLc8dkJOGXkIDTFTWyvixzSe7U3NcUdOXIkpk2bhp/97GcIh8PYtWsXHnjgASxcuPCY/+6+ThJtW3j3Qc3NzfjWt76Fu+++G0VFRQCA8vJyzJ8/H6tXr8ann36KRYsWYdWqVem0o4cffhhvv/02nnrqqYOuPxwOY9q0aVizZk2XInVERP2V7Qg0Ro122SMpdeEE/vBGJd7eXJdx//A8H7517licNCy3w/WqsptiyvKaruO56ejrb/u0df+FhOn2Xygrykr33TjYcqLWXvxoDx56awt2NcQQt2wIAeiKhByfhoTlwLTdzApVkTA0x4evzhmN+ZOHwnFEj5Y4dfQ+H+TXIQA0Ro0uvffd8a02IoaNqGHDtp12Y4ttR2DtzkasKK/BOxV1iLaZ1pZSlOXB2eOLMHd8EUYXBtLZBZLkBl28mtvvxKcpUDtrBETHxJG+V4/FMfVwzkt1dXX4yU9+glWrVkGWZXzuc5/DrbfeCkVhf7cD6TOBkXg8jurq6g6XFRYWwu/3Z9z3+9//Hi+++CJWrFiBv/zlL3jxxRfxwgsvpJevWLECt99+Oz744IN26zMMA4ZhpG+Hw2HMmTOn33xQIiI6UhHDQmPE6DTT479b6vD71yszUowB4IITB+OGM0Yjy9u+fEYCoKsKcgMaAnq/qfQ8ZvrbRXxv0B/36cE+6Pf0RSv1LZblYOWWOlS3xCEEUFocRJ5fx7AcH97dVo/aUAKFWR7MGlPQbqRxT+rofQ7gsN77tiMQTVgIGxYSpt3hlwQJ08aqbQ1YXl6D97bWw7Q7vtw6Lt+PueOLcPb4IgzJ3d8wU4IbJPHoCgKa25dEY5CkTzjax9T+eF7qrfrMJ8+PP/4YX/rSlzpcdv/99+Occ85J3/7rX/+KRx99FA8++CCAzrvzRqPRDtf30EMP4Q9/+MNR2nIiov4noKvwqkqn2SMzxxRg8vBcPLpyO5au3ZP+Zm3Z+n14d0s9Fp1VirPGFWbW4cItr6ltsRHxqMj16R2W7hBR1x2s7wb7ctChUFUZc8YVdbiss/t7g077kBzGe1+RJWT5NGT5NCQsG5GEjahhwbQdpL5u9mgKzhhbiDPGFiKcsPCfijosL6/BhzsbM86XO+qjeHTldjy6cjuOL8nC3PFFOHNcEQYFdNjCDcDEEhZkSYKuyQjoCryaynNjL8Zjat/VZwIjM2bMwKZNmw74GMMwcPfdd2PZsmV46KGHcOqppwLovDtvZ515b7jhBlx77bXp26mMESIi2k+RJRQEPfDpSofZI35dxU1nleKc44vw61c3p0cdNkZN/O8rG/Hqhmp8c24ZBud4M57nJOu946aDbK+KbC/La4iIqPfxqAo8qoI8v4aoaSOaDJI4jkh/IRD0qPjMxMH4zMTBaIgYeHNTDZaX12BjVShjXRurQthYFcIDb27BlBF5mDu+CLPLChD0qLCFQMywETdsSJIJXd0/BtijsjyC6GjoM4GRg2loaMCNN94IwzDw3HPPpafRAG533u3bt8OyLKiq+5IrKytRVlbW4bp0Xe90BBIREWVKZY80RQ2EOsgeGT84Gw9eMRXPrdmNv7y7A4lkAGX1tgZ8+fH3cc2skbhk6jAobYIflu2gMWIgkrBZXkNERL2WJEkI6CoCugrL1hA1LIQTNgwrs9TGbcI5DAumDsOephjeKHeDJDvq92exOwJYs6MRa3Y04revb8apo/Nx9vginDpqEDyaku53kjBtNEXdElRfsi+JR2OQhOhw9YtPmaZp4rrrrkNeXh7uv/9+eL2Z3z7OmDEDeXl5+PWvf41vfvOb2LZtG5588knccsstPbTFRET9iyJLyA964E1mj5hWZmM6VZFx2fQROGNsIX73egU+2NEIAIhbDv741la8vrEGt547FmOLM0fJsbyGiIj6ElWRke3Tke1ze41EEjYihgXL2V9qAwBDc3248tTjcMWMEdhaG8Hy8hqsKK9BTWh/by7TFninog7vVNTBrys4vawAZ48vwtQReVBkCUIAcdNG3LTRHAM0xc0i8Sf7khBR1/WZ5qsH8uqrr2Lx4sXweDztuu2+8sorGDJkCHbs2IGf/OQn+Pjjj+H3+3HllVfiq1/9apfWz6Y3RERdZzui0+wRwO3uv7y8Bg+8sQVNMTN9vywBl0wdhmtmjoSvkw90qiIjOzm9pnV/koGI56ajj/uUiI4FIQSipo1IwkIsYcMRot1UGwBwhMAne5qxorwWb26qQUvc6nB9eX4Nc8YWYu7xRZhQkt3ufChL7vkylUni1ZQBf87sq3he6j79IjByrPENSUR06KKGhcaICcOyO/wA2Bwz8dBbW/H/Pt2XcX9xtgffmFuGU0fnd7heCW5ju1y/Bv8ALq/hueno4z4lomPNsp1OS23aPm7NzkYs31iD/1TWIW52PAVucLYXc493J9uMKmjfP1GWAEV2e5J4dQU+VWHfrj6E56XuM3A/URIR0THl11V4DtB7JMen4fbPjMM5E4rw29cqsKfJbZJd3ZLA95d+grPGFWLRWaUYFMjs+STgpg7XtNgIeDTk+jWOMSQioj6hdalN3HDLbCKGDbtNqY2qyJgxKh8zRuUjbtp4d0s9lpfXYPW2BlitTqj7WuJ4etVOPL1qJ0YXBnD2uCKcfXwRBme7rQUcATi2g+aYg5a4uT9IosnwayqDJERJDIwQEdExk+o94tcVNHSSPTJ1RB7+fPXJePK9Hfjb+7tgJz/wvbGpFu9vb8RXzxiNC04cDLlNGrAjgFDcRMy0keNVkc3yGiIi6kO8ySyOPEcgalqIJNzJM21LbbyagrPGF+Gs8UVoiZl4Jzn+9+NdTRmP21obwdbabfjTf7Zh4pBszD2+CHPGFiLX737BIISbidIScxCKA4pswqcp8OkyfJrargk60UDCwAgRER1zPl1FiaqgKWqiJW60yx7RVRlfmT0KZ493R/tuqGoBAIQTFn7z2ma8vrEa3zpnLEbk+9ut27IdNEQMRAwbeX4NvgFcXkNERH2PLEsIejQEPRoMyy21iSRsmHb7Uptsn4YLJ5XgwkklqA0l8ObmWqzYWINN1Znjfz/Z24JP9rbgvhWVOPm4PJx9fDFml+anS1BTQZKQ7SCcAGTJgDfZk8SnKVCZiUkDDD89EhFRt5BlCYOCOvy6gvqI0WH2yKiCAO69fDJe/rgKf3pnKyKGDQBYt7sZ1z/5Aa6YMQKXnTKi3WSaVHlNNctriIioD9NVGbqqI9cPxAwLUcN2S21sp905szDLgy9MG4YvTBuGXQ1RrEiO/93dGEs/xhHA6u2NWL29EboqY+bofMw9vginjByUPpcKAdhCIJKwEE1YkCUJHl2BX5Ph01WeT2lAYGCEiIi6lVdXUKJ6k9kjJpw2PcBlScL8yUMwc0w+/vBGJd6pqAPgji18/L87sKK8Ft+aV4ZJw3LbrZvlNURE1F/4dBU+XUWuI5INWy0kzI4btg4f5MfVM0fiS6cdh4qaMJZvrMEbm2pQFzbSjzEsB29ursWbm2sR9Kg4o6wAZx9fhJOG5abLaATcIEk0YSGWAGTJhK7J6RHAHpVjgKl/YmCEiIi6XevskYaogYTZPnukMMuDH198AlZW1uHe5ZWoDScAADsbovjmMx/js5NKcP3po5Dl1dqtn+U1RETUXyiyhCyvhiyvhoRlI5pwm7ZattMuSCJJEsYWZ2FscRZumDMa63Y3Y0V5Dd7aXItQq/G/4YSFZZ/sw7JP9iE/oOOs8YU4e3wRxhVnpb9QSAVJYobb+0SKArq6v9zGozFIQv0HPykSEVGP8eoKBqteNMVMhGIm7A4myM8qLcDk4bl4dOV2/OPDPekAyj/XVWFlZR0Wn12KOWML22WGtC6vCXo15PhYXkNERH2bR3WzNnL9GmKmjUjCRtSw4Dii3RcMsiRh8vBcTB6ei8Vnl+L97Q1YvrEG/91Sj4S1f/xvfcTAc2v24Lk1ezA014e5493xv637egm4JTdx00bCtNEkAZriBkn8OoMk1PcxMEJERD1KliUMCugIJHuPdJQ9EvCoWHx2Kc45vgi/fm0zttZGAACNURM/+edGnDq6GjfPLUuPJ2zNEUBLzETUsJHrU5HlZXkNERH1bZIkwa+r8OsqLFtLltrYMKyOS200RcbMMQWYOaYAMcPGyi11WL6xBu9vb8h4/J6mGJ54bweeeG8HSouC6SBJYZYn/ZhUkCRh2UhYNppjbpDEpyvpkhuivoaBESIi6hU8moKSnGTvkU6yR44vycYfr5iKZ9fsxl/e3QEj+Y3Xe1sb8NGu9/HlWaPw+SlDOxw5aNkO6iMGwgkbg/w6P7gREVG/oCoysn06sn1AIplFEjEsWI6DDk6l8OkKzjm+GOccX4zmqIm3KmqxfGMN1u9pznhcZU0YlTVhPPz2VkwaloOzxxfhjLGFyPFllrA6rYIkLZK7PX5NTWaSyPwygvoEBkaIiKjXkCQJeYH9k2s6yh5RFRmXTx+BM8YW4nevbcaanU0AgLjp4IE3t+D1jdX49ryxKCvOarf+VBrwvpYYspLlNRxJSERE/YUn2fsjT2iIJKfaxBI2HNG+1AYAcvwaLj5pCC4+aQhqWuJYsckd/1tZG04/RgD4eHczPt7djHtXVOKUkXmYO74YM0vz4WtTQuMIt8mrYRloibvnbF9yDLBXUxgkoV6LgREiIup1UtkjzTETzdGOs0eG5vrwi4WT8NrGGjz45hY0x0wAwObqMG58ei0WThuGq2eObPehDXA/uDUny2tyWF5DRET9jCRJCHo0BD0aTL+DaMIttTHtjkttAKAo24vLThmOy04Zju31ESzfWIMV5TWoao6nH2M7Au9tbcB7WxvgVWXMLC3A3PFFOHlkXrs+XvuDJA5CcROKLLuNW5PNW3nepd6EgREiIuqVJElCrl+HT+s8e0SSJJw7oRgzRg7CH9/egn9/Wg3A/TD29w924+3NdfjmOWWYPmpQh7/DZHkNERH1c5oiI8evI8cPxA23zCZi2LA7KbUBgJH5AXxl9ih8edZIlO8LYXl5Dd4or0Fj1Ew/Jm45WFHuBk+yvSrmjHUn25w4LAdym6CHIwDHdtAcywySeHUFPlWB3EEJLFF3YmCEiIh6tdbZIy0xt2a6rRy/hu98ZjzmHV+M37y+GXub3G+39rXE8d0X1uPs8UX4+pljMCigt3suy2uIiGig8CaDEXmOQNR0s0jihg3RSamNJEk4viQbx5dk48Y5Y/DRriasKK/B2xW1iCTs9ONa4hZeXleFl9dVoTDowVnjCzF3fBFKi4LtMkNaB0laWgdJNLc3CYMk1BMYGCEiol6vdfZIQ9RA3LQ7/JZr6nF5+POXTsaT7+3AMx/shp3MF15R7nbe/9oZo/GZiYM7TN9tXV6T69MQ9KpM8yUion5JlveX2hiWg0jCchu22k6npTaKLGHacXmYdlwevjG3DKu2NWB5eTXe3VIP097/pNpwAn//YDf+/sFuDM/zYe7x7mSbYXn+dusUwm2O3hJzEIoDimzCpynw6TJ8mtphM3WiY4GBESIi6jM8moLB2V60xEw0d5I94tEUXHf6aJw9vgi/eW0zNlSFAAChuIVfvroZr22sxi3njMXwQe0/oAFueU1dJIFQwkJ+QIengx4lRERE/YWuytBVHbl+DTHTRtRwf2zb6TCLJPWc08sKcHpZASIJCysr6/D6xhqs3dmYEVjZ1RjD4//dgcf/uwPjBmdh7vginDWuEPlBT7t1poIkIdtBOAHIkgGvtn8EcNseJkRHEwMjRETUp0iShJxkP5CGSOfZI6MLg/j9ZVPw8sd78af/bEPUcFN+P9rVjOue+ABXzjgOl00f3uEHrVR5TVVzHFleleU1RETU70mSBL+uwq+rsB2BaMJCKGHBsDpv2AoAAY+Kc08YjHNPGIyGiIG3NrvjfzdUtWQ8btO+EDbtC+HBN7dg8ohczB1fhNPLCpDl1dqtUwjAFgKRhIVowoIsSfDoCvyaDK+mQld5Tqaji4ERIiLqkzxqq+yRuJv+25YiS/jclKGYVVqA+1ZU4j+VdQAA0xZ47L/bsWJTDb49bywmDs3p8Hc4QmSU12T52n94IyIi6m8UWUJW8ryXMG1EDBuRhJup2VnDVgAYFNDx+SlD8fkpQ1HVHMMb5bV4fWM1ttdH048RAD7c2YQPdzbh98srMH3UIMwdX4zTRg/qMEtTwA2SRBMWYglAkkzoanLCTXI8MdGRYmCEiIj6rNbZI41REzHD6vADW2GWBz+ZfwLeqajDvSsqUB82AAA76qO4+W8f4aJJJbj+9NEIejs+LabKa8KGhUF+ltcQEdHA4UkGH/L8GqKmGyCJJWw4nTRsTSnJ8eF/ZozA/8wYga21YSxPTrCpbkmkH2PaAisr67Gysh4+TcHsMnf879QRuR1magoAQgjETRsJ00aTBGiKAr/u/vD8TIeLgREiIurzPKqC4iwZobiMpljH2SMAcHpZAaaOyMWf/rMNL320N/2B7uV1VVi5pR6Lzy7FGWUFHTZdFQKIGTaqTLe8JtevsykcERENGJIkIaCrCOgqLL+DqOFOtTlYqQ3glreOLgziutmj8OneFiwvr8Fbm2rRFNs//jdm2nhtQzVe21CNXJ+GOePcyTYThmS3G/8LpIIkQMKykbBsNMcAVXEn27hBEplN1KnLGBghIqJ+QZIkZPt0eDUVDVGj0+yRgEfFN+aWYd7xxfj1a5uxrS4CAGiIGPjxyxtw2uh8fGNuKYqyvR3+nozyGr/WYW00ERFRf6YqMrJ9OrJ9QMK0EU5YiBr2QUttJEnCxKE5mDg0B4vOHIO1O93xv+9U1CFm7h//2xQz8eJHe/HiR3tRnO3B2eOLMHd8EUYXBjtdtyMAw3JgWAZa4u42+jQlOQpYYZCEDoiBESIi6ld0VU72HjEOmD0yYUg2HrpyKv7+wW785d3t6VGD726tx0e7mvDl2SPxuclDO80KMW0HdeEEwgmW1xAR0cCVLrVxBKKmhUjCRtw4eKmNqsiYPmoQpo8ahFvOsfHuVnf87+ptDRnjf6tbEvjr6l346+pdGJnvT4//Lcnxdbru/UESB6G4CUV2e5J4dQU+VYHMjE9qg4ERIiLql7qSPaIqMv5nxgicMbYAv329Ah/ubALgpvPe/8YWvL7Rbc5aWtTxN1QsryEiInLJsoSgR0PQo8Gw3FKbSMKGaR+81MajKThzXCHOHFeIcNzC2xW1WFFeg492NWU8d3t9FH/+z3b8+T/bMaEkC2ePL8aZ4woxKKB3um5HAI7toDnmoKV1kERzy24YJCGAgREiIurHUtkjoZiJxpjZafbIsDw/frVwEl7dUI0H39yClrgFwB0t+LWn1uAL04bh6pkj4e0kK4TTa4iIiPbTVRm6qiPXD8QMt8wmYtiwD1JqAwBBr4oLTizBBSeWoD6cwJvJ8b/l+0IZj9tQFcKGqhAeeLMSU0fkYe7xRZhdWoCAp/NLXCEAy3bQEnMQigOKbMKnKfDpMnyayi83BjAGRoiIqN/L8mnwaAoaowainWSPSJKE804YjBmjBuHBt7bitQ3VANxvmp75YDferqjDN88pwykjB3X6ezi9hoiIKJNPV+HTVeQ6ws0iMfaX2hxMftCDS6YOwyVTh2FPYwwrymuwvLwGOxv2j/91BPDBjkZ8sKMRv1E247Qx+Th7fBFOHZUPXW0/2SYlFSQJ2Q7CCUCRTHh1ty+JT1M6nIpD/Rf/2kRENCDoqozibC8KAp4DftjJ9ev43vnj8YtLTkRJzv4GrFXNcXzn+fX42bKNaIwanT4/XV7THEd9ONFplgoREdFAosgSsrwaBmd7UZLrRZ5fh67K6GqSxtA8H6467Tg8ds3JePiqafjiycNQGPRkPMa0Bd7eXIe7XtqASx78L37x/zZhzY5G2Aep5RECsBwH4biFulACexpjqG6JIxQzYfaj8/jHH3+M8ePHY8qUKemfK664Ir1827ZtuPrqqzFlyhTMnj0bf/zjHzOe/9Zbb+Giiy7C5MmTcf755+ONN97IWP7II4/gjDPOwOTJk3HVVVdh69at6WXRaBTf+973MGPGDEybNg233347IpHIsX3Bh4CBESIiGlCyfBpKcrwIeNQDfhg7eeQg/Pnqk3H59OEZj3t9Yw2ufex9/OuTfRAH+LYrVV5T1ex+sDrQY4mIiAYSj6ogL6BjaK4PRdleZHk1KLKErsRIJElCaVEQX5szBn/96gz89tKTcNFJJcj2ZhZDRAwb/+/TfbjtuXX44kPv4g8rKrGxquWg52MBwBYCkYSFunACextj2NcSR0vMgGH17SDJ+vXrccopp+DDDz9M/zz99NMAANM08bWvfQ0nnngiVq1ahYcffhhPP/00/vWvfwEAtm/fjsWLF+Mb3/gGPvjgAyxevBjf/OY3UV3tZtguXboUTz75JP785z9j1apVOOGEE3DzzTen9/eSJUtQVVWFf//733j11VdRVVWFX/3qVz2zIzrAwAgREQ04muJmj+QHPdAOkD3i1RRcf/poPHTlNIwfnJW+vyVu4Zf/3oRvP7sOu1ql83YkVV6zryWORKtRhERERAOdJEnw6yoKszwYmutDflCHV1O6nEUiSxJOGpaLW84Zi2e/dhp+9vmJOOf4Ini1zHN7Y9TECx/uwaL/+xBX/nk1Hl25DdvrD56tkAqSRBMW6sMG9jbFUNUcQ3PUQMLqe+f09evXY+LEiR0ue//991FTU4Obb74Zuq5jwoQJuOqqq9KBk6VLl+Lkk0/GOeecA1VVccEFF+CUU07BM888AwD4+9//jv/5n/9BWVkZPB4Pvv3tb2Pv3r1YtWoVYrEYXn75Zdx8883Izc1Ffn4+br31VrzwwguIxWLd9voPhD1GiIhowMryavCmeo8krE675o8pCuK+y6fgxY/24s//2YZYMsDx0a4mXPfEB7jq1ONw6SnDOw2ypMpr9llxDM72svcIERFRG6oiI9unI9sHJEwbkYSNiGHB6kLDVsD90uPU0fk4dXQ+YqaN/1bWY0V5DVZvb8gopalqjuOp93biqfd2YkxhAHPHF+Gs8UUozvYeYO1ukEQIgViyR4oUBXRVgV93e5Icy3N7OBzOuK3rOnS9/SSeeDyezuBoq7CwEOvXr0dBQQHOPfdchMNhTJ8+Hd/97ncxePBgVFRUYNSoURnrLS0txcMPPwwAqKysxNixYzPWWVpaivLy8vTy66+/Pr1M0zSMHDkS5eXlyM3NhWmaGc8fM2YM4vE4tm/fjuOPP/4Q98jR1y8DI7fddhv27duHJ598Mn3ftm3bcNddd2HdunUIBAK48sor8bWvfa0Ht5KIiHoDTZFRlOVFWDfRGOm8lliRJSyYOhSzS/Nx74pK/HdLPQC3nvnRlduxorwG35o3FhOH5nT6uxwhutRsjoiIaCDzJAMNeUJDJDn2N9WwtStnUZ+mYO7xRZh7fBGaYybeSY7//XhXc8bzt9RGsKV2Gx5+ZxtOHJqDuccXYU5ZIXL8B54u5wZJgLhpI2HaaJIATXGDJH796AdJ5syZk3H7pptuwuLFi9s97uOPP8aXvvSlDtdx7733oqioCDNnzsTll18O0zSxZMkSfPWrX8XSpUsRiUTg8/kynuPz+RCNupmxHS33er1dWp4K7Pj9/ox1p57XG/S7wMhzzz2Hf/7znzj55JPT96XqpebNm4dHHnkElZWVuOGGG3Dcccfh/PPP78GtJSKi3iLo0eBRD549UpTtxZL5J+Cdyjrct7wS9RG3Eev2+ii+8bePcNFJQ3Dd6aMQPMC4QCIiIjo4SZIQ9GgIejSYtoNowkI4YcO07U7P023l+DR8dtIQfHbSENSGEnhjUw1e31iDyprMLIz1e5qxfk8z7ltRiZOPc8f/zhpTAJ9+4CBHKkiSsGwkLBvNMSDHpyMv0D6j43C99dZbCAaD6dsdZYsAwIwZM7Bp06ZO13Peeedl3L7jjjtw2mmnYcuWLfD7/e3KWmKxGAKBAAA3kBGPxzOWx+PxLi1PBURary/1u1q/rp7Ur3qMVFZW4oEHHsAXvvCFjPsPVi9FREQE7M8eKcg6cO8RSZJwRlkhHrv2FFx80pD0/QLASx/vxbWPvY+3K2rZcJWIiOgo0RQZOX4dQ/N8GJztQ45Pg6rIkLrYjwQACrM8+OLJw/HwVdPw+LWn4EunHYdheZlZDrYjsGpbA362rBwLHvwvlvxzA1ZW1nV5Ok1XAzaHIhgMZvx0Fhg5kKqqKtx9990ZGRqG4X654/V6UVZWhu3bt8OyrPTyyspKlJWVAQDGjh2LioqKjHW2Xl5WVpax3DRNbN++HWPHjsWoUaOgaRoqKyvTy7ds2ZIut+kN+kxgJB6PY8eOHR3+RKNRxONx3HLLLbjzzjtRWFiY8dzO6qVS9VBtGYaBcDic8UNERANH0ONOrsnyagdsABf0qPjmOWW497LJOC5/f3pofcTAXS9twI9e/BS1oUQ3bDEREdHA4dUV5Ac9GJbrQ2GWB36PCvlQIiQARgzy45qZI/GXa0/Bg1dMxcJpQ5HfJssjYTl4Y1Mt7njxUyz847v49aub8dGupoOO/+2N8vLy8Morr+C3v/0tEokEGhoa8OMf/xinnXYaRowYgRkzZiAvLw+//vWvkUgkUF5ejieffBILFy4EAFx88cVYvXo1li1bBsuysGzZMqxevRrz588HAFxyySV46qmnUF5ejkQigV//+tcoKCjAySefDJ/Ph/PPPx+/+tWv0NDQgIaGBvzqV7/CZz/7WXi9B+7t0l36TJ7vgeql7r//fqxYsQKzZs3CnDlzsG7duozlB6uXauuhhx7CH/7wh6Oz4URE1CepiozCLA98unzA3iMAMHFoDh6+ahr+9v4uPPXeDpi2+4Fp5ZZ6rN3ZhK/MHoX5k4dAVQ7tQxsRERF1Tpb3l9oYloNIwnIbttpOlzM3JEnCuMFZGDc4CzecMQYf727CivIavL25DuHE/uyJUNzCK+ur8Mr6KuQHdZw9zu1hUlYUhHSIQZme4PV68ac//Qk///nPMXv2bADAmWeeibvvvhsAoKoqHn30UfzkJz/BrFmz4Pf7cdVVV2HBggUA3Gap999/P371q1/hBz/4AYYOHYr77rsPo0aNAgAsXLgQoVAIixYtQkNDA0488UQ89NBD0DS3X8udd96Jn//857joootgmibmzp2LO+64owf2RMck0Q/yfF966SU8/vjj+Nvf/gZd13Hfffdh9erV6earjz/+OF566SW88MIL6eesWLEC3/nOd/D++++3W59hGOm0IsDtAjxnzhysWbOm19RAERFR97FsB41RE5GEedAPWrsaovjt65vx0a7mjPvHD87CreeNTdYrH/n3EuFwGNOmTeO56SjiPiUi6vuEEIiZNqKG+2PbTpcatrZlWA7e396A5Rtr8O7WeiSsjr8gGZbnw9njizB3fBGGD3KzR/P8R6fHCM9L3afPZIwcyIsvvoht27Zh5syZAIBEIgHbtnHyySfjpZdeyqiXUlX3Jbeuh2qrs/FHREQ0MLXOHmmKmjA6+XAEAMMH+fHrL5yE//dpNf741haE4u63TeX7QrjhybX4+plj8O1zx3XXphMREQ0okiTBr6vw6ypsRyCasBBKWDCsrjdsBQBdlTGrtACzSgsQNSysrKzH8vIafLC9IWM9uxtjeOLdHXji3R0YWxzE3PFF+NyUoUe1+Sode/0iMPLnP/8543bbjJGioqJ0vdQ3v/lNbNu2DU8++SRuueWWnthcIiLqo4IeDV5VOWj2iCRJOH/iYJw6ehAeeGMLlpfXAHAbut23ohJnjivCtOPyunHLiYiIBh5FlpDl05Dl05AwbUQMG5GEBctxcCh1E35dxbwJxZg3oRhNUQNvbXbH/67f05LxuM3VYWyuDuOxldvx2LXTcdqY/KP8iuhY6ReBkYM5WL0UERFRVx1K9kieX8cPLjwe555QjN++VoF9LXGosoRcv9aNW0xEREQeTYFHU5Dn1xA13QBJLGHDEeKQSm1y/TrmTx6K+ZOHYl9LHG+W12B5eQ221O6f9hK3HLyxqYaBkT6kXwZGFi9e3O6+4447rl1mCRER0eFKZY80xUyE4wfuPXLKyEH48zUnY/W2BkwenoMxhawTJiIi6gmSJCGgqwjoKix/qmGrfcilNgAwONuLy6aPwGXTR2BbXQQrymvwn8o66IqML0wbdmxeAB0T/TIwQkRE1B1URUZB0AOfrqAxYhwwe8SnKThzXCEGZ/eOsXREREQDnarIyPHryPEDCdNGOBkksQ+x1AYARhUE8JXZo/CV2aOOWvNV6j4MjBARER2hgK662SNRA6GDZI8QERFR75MutXEEoqaFcMJGwjj0UhvqmxgYISIiOgoUWUJ+0ANvF7JHiIiIqHeSZQlBj4agR4NhOYgaFiIJG6Z96KU21HcwMEJERHQUMXuEiIiof9BVGbqqI9cPxAy3zCZq2LBth1kk/QwDI0REREdZKnvEpytoiBgwLX6AIiIi6st8ugqfrsJ2BKKGhXDCQsJkFkl/wcAIERHRMeLXVXhaZY8QERFR36bIErK8GrK8GhKWjUjCRtSwYNkOgyR9GAMjREREx1Dr7JGmCIMjRERE/YVHVeBRFeT5NURNG9FkkIT6HgZGiIiIukEqe0Qc6vw/IiIi6tUkSUJAVxHQVVi2BpupI30OAyNERETdRJElAFJPbwYREREdI6oiQ1V6eivoUMk9vQFERERERERERD2FgREiIiIiIiIiGrAYGCEiIiIiIiKiAYuBESIiIiIiIiIasBgYISIiIiIiIqIBi4ERIiIiIiIiIhqwGBghIiIiIiIiogGLgREiIiIiIiIiGrAYGCEiIiIiIiKiAYuBESIiIiIiIiIasNSe3oC+QAgBAAiHwz28JURERK7UOSl1jqIjx/M9ERH1JjzXdx8GRrogEokAAObMmdPDW0JERJQpEokgKyurpzejX+D5noiIeiOe6489STD8dFCO46CmpgaBQACSJPX05hAREUEIgUgkgqKiIsgyK2OPBp7viYioN+G5vvswMEJEREREREREAxbDTkREREREREQ0YDEwQkREREREREQDFgMjRERERERERDRgMTBCRERERERERAMWAyNERERERERENGAxMEJEREREREREAxYDI0REREREREQ0YDEwcowtW7YMEyZMwJQpU9I/t912GwDg448/xhe+8AVMmTIFZ599Np599tmM5y5duhTz5s3D5MmTsWDBAnz44YfpZbZt4+c//zlmzpyJKVOm4MYbb0RNTU23vrZD1dDQgHnz5mHVqlXp+47lPqivr8fXv/51nHzyyZgxYwZ++tOfwrKsY/9Cu6ij/XHnnXdi4sSJGe+XZ555Jr28P+6P8vJyXHvttZg+fTpmzZqF22+/HQ0NDQAG5vvjQPtjIL4/AODdd9/FF77wBUydOhWzZs3CkiVLEI/HAQzM9wj1Tjzfu3iuz8RzvYvn+vZ4vs/Ecz31OEHH1D333CO++93vtru/qalJTJ8+XTz11FPCNE3x3//+V0yZMkV8/PHHQggh3nvvPTFlyhTxwQcfCMMwxGOPPSZmzJghotGoEEKI++67T1x00UVi7969IhQKiW9+85vi+uuv79bXdig++OADcc4554ixY8eK9957Twhx7PfBlVdeKb797W+LaDQqdu7cKS688ELxyCOPdP+L70BH+0MIIT7/+c+LF154ocPn9Mf9EYvFxKxZs8Tvf/97kUgkRENDg7j++uvFDTfcMCDfHwfaH0IMvPeHEELU19eLE088UTz//PPCtm1RXV0tPvvZz4rf//73A/I9Qr0Xz/c817fFc72L5/r2eL7PxHM99QYMjBxjV1xxhXjqqafa3f/3v/9dnHvuuRn3/ehHPxK33367EEKIb3/72+KHP/xhxvLPfOYz4rnnnhNCCHHGGWeIl156Kb2strZWjBs3TuzcufNov4Qj9sILL4gzzzxTvPLKKxkfDo7lPti+fbsYO3as2LdvX3r5K6+8Is4888xj8hoPRWf7I5FIiBNOOEFs3ry5w+f1x/2xZcsW8ZWvfEVYlpW+7/XXXxdTp04dkO+PA+2Pgfj+SAmFQkIIIRzHEZs2bRLz5s0TTz755IB8j1DvNdDP9zzXZ+K5fj+e69vj+b49nuupp7GU5hhyHAeffvop3nzzTZx11lk444wzcMcdd6C5uRkVFRUYO3ZsxuNLS0tRXl4OAKisrOx0eSgUwr59+zKWFxQUICcnB5s2bTr2L+wQzZ49G6+99houuOCCjPuP5T6oqKhAbm4uiouL08vHjBmDvXv3oqWl5Wi/xEPS2f4oLy+HZVm49957MXPmTJx33nl4+OGH4TgOgP65P0aPHo0//elPUBQlfd+///1vnHDCCQPy/XGg/TEQ3x8pwWAQADBnzhxcdNFFKCwsxIIFCwbke4R6J57vea5vi+f6/Xiub4/n+/Z4rqeexsDIMdTQ0IAJEybgvPPOw7Jly/C3v/0N27dvx2233YZIJAKfz5fxeK/Xi2g0CgAHXB6JRAAAfr+/3fLUst6ksLAQqqq2u/9Y7oOOnpu6nVp/T+lsf4RCIUyfPh1XXXUV3nrrLfzyl7/Ek08+iUcffRRA/90fKUII/Pa3v8Ubb7yBH/zgBwP2/ZHSdn8M9PcHALz66qt4++23Icsybr755gH/HqHeg+d7nuvb4rm+YzzXt8fzfSae66mnMDByDBUUFODpp5/GwoUL4fP5MGTIENx22214++23IYRINxRKicfjCAQCANx/lJ0tT/2DjcVinT6/LzjQazzY8oPtA7/f325Z6nZv3UezZs3CE088genTp0PTNEyaNAlXX301li1bBqB/749wOIybb74ZL7/8Mp566imMGzduQL8/OtofA/n9keL1elFcXIzbbrsN77zzzoB+j1DvwvN95/jvNNNAPpbzXN8ez/ft8VxPPYWBkWOovLwcv/rVryCESN9nGAZkWcakSZNQUVGR8fjKykqUlZUBAMrKyjpdnpOTg+LiYlRWVqaX1dbWoqmpqV0qWW82duzYY7YPysrK0NTUhLq6uvTyLVu2YPDgwcjKyjqGr+rwvf766/jb3/6WcZ9hGPB6vQD67/7YuXMnLrnkEoTDYTz33HMYN24cgIH7/uhsfwzU98fatWvxmc98BoZhpO8zDAOapqG0tHRAvkeo9+H5vnMD9VjemYF6LOe5vj2e7/fjuZ56hZ5rb9L/VVVVicmTJ4uHH35YmKYp9uzZI774xS+K73//+6KhoUGcfPLJ4rHHHhOGYYh3331XTJkyRbz77rtCCJHuuPzuu++mOyyfcsoporGxUQghxG9/+1vx2c9+VuzcuTPdYfnKK6/swVfbNa0bkB3rfXD55ZeLW265RYRCoXSX6XvvvbfbX/OBtN4fr776qpg0aZL473//KxzHEWvXrhUzZswQ//jHP4QQ/XN/NDU1iTPPPFN897vfFbZtZywbiO+PA+2Pgfj+EEKIcDgs5syZI372s5+JRCIhdu/eLRYuXCjuvPPOAfkeod6J5/tMPNdn4rme5/q2eL7PxHM99QYMjBxjq1atEpdeeqmYMmWKOPXUU8WSJUtEPB4XQgixbt269LK5c+eK559/PuO5//jHP8R5550nJk+eLBYuXCg++uij9DLDMMQvf/lLcfrpp4upU6eKG2+8UdTV1XXrazscbUfWHct9UFtbKxYvXiymT58uTj31VHHPPfdkdP/uDdruj7/+9a/i3HPPFSeddJKYO3duuwkH/W1/PProo2Ls2LHipJNOEpMnT874EWLgvT8Otj8G2vsjpaKiQlx77bXi5JNPFmeddZb4zW9+IxKJhBBi4L1HqPfi+X4/nusz8VzPc31bPN+3x3M99TRJiFZ5n0REREREREREAwh7jBARERERERHRgMXACBERERERERENWAyMEBEREREREdGAxcAIEREREREREQ1YDIwQERERERER0YDFwAgRERERERERDVgMjBARERERERHRgMXACBEd0H333Ydx48a1+5k4cSJmz56NRYsWYe3atQddz6pVqzBu3Djcd9993bDVRERE1FU81xPRQKf29AYQUd9w6aWXYtq0aenblmVh7969+L//+z+8+eab+OMf/4jTTz+90+ePGTMGv/jFLzBu3Lju2FwiIiI6RDzXE9FAxcAIEXXJ5MmTMX/+/Hb3n3322bjkkkvwi1/84oAflgoKCjp8PhEREfUOPNcT0UDFUhoiOiInnHACysrKsHnzZjQ3N/f05hAREdFRxnM9EfV3DIwQ0RGTZfdQYts2rrrqKnz2s5/Fc889h5kzZ2Ly5Mn4zW9+02nd8fr167F48WKcdtppmDJlCubPn49nnnkGQoiMx73zzjv40pe+hKlTp+Kkk07CggUL8MILL3TbayQiIhrIeK4nov6MpTREdESqqqqwdetWDB06FIMGDQIA7N69Gz//+c/x1a9+FY7jYNq0abBtu91z33nnHdx4440IBoO4/PLLUVRUhNdeew0/+tGPsHfvXtxyyy0AgKeffhpLlizBiSeeiJtuugmyLGP58uX43ve+h40bN+IHP/hBt75mIiKigYTneiLq7xgYIaIuiUajaGhoSN82TRPl5eX4/e9/D8MwsGjRovSyWCyG2267DVdccUX6vlWrVmWsTwiBO++8E4FAAC+++CKKi4sBuI3frrzySjz66KO49tprEY/Hcffdd+PMM8/Egw8+CEmSAABXX301vvOd7+CJJ57ARRddhEmTJh3Ll09ERNTv8VxPRAMVAyNE1CVLlizBkiVL2t1fWFiIH/3oR7jkkksy7p87d+4B17dhwwbs2bMHX/rSl9IflAA3VfeXv/wlEokEgsEgXnrpJZimifPPPx+NjY0Z67jwwgvx4osv4tVXX+WHJSIioiPEcz0RDVQMjBBRl3zlK1/B7Nmz07d1XUdhYSFGjBiR/mantYKCggOub/fu3QDc0X5tDR06NP3f27ZtAwDcfvvtna5rz549B954IiIiOiie64looGJghIi6pLS0FDNnzuzy41X1wIcXy7IAoMMPWq2l6pWXLFmCYcOGdfiYVL0zERERHT6e64looGJghIh6ROqDT+pbotZWrlyJf/zjH7juuuvSj8vOzm73Ya2mpgbr1q3D8OHDj/0GExER0SHhuZ6I+gqO6yWiHnHCCSdg8ODBePnllzMavQkh8Oc//xmvvPIKCgsLce6550KWZfzxj39EPB7PWMc999yDRYsW4ZNPPunuzSciIqKD4LmeiPoKZowQUY9QVRU//vGPsWjRIsyfPx+XXXYZ8vLy8Prrr2PlypW49dZbMWjQIAwaNAiLFy/G73//e8yfPx+f//znkZ2djeXLl+M///kPzjrrLJx77rk9/XKIiIioDZ7riaivYGCEiHrMmWeeiaeffhoPPPAAHnvsMdi2jTFjxuA3v/kNLrzwwvTjvv71r6O0tBRPPPEEHn74YTiOg+HDh+P222/HVVddBUVRevBVEBERUWd4rieivkASQoie3ggiIiIiIiIiop7AHiNERERERERENGAxMEJEREREREREAxYDI0REREREREQ0YDEwQkREREREREQDFgMjRERERERERDRgMTBCRERERERERAMWAyNERERERERENGAxMEJEREREREREAxYDI0REREREREQ0YDEwQkRERERERNSPNDQ0YN68eVi1alX6vvLyclx99dWYMmUKZs6cibvvvhuWZaWXL126FPPmzcPkyZOxYMECfPjhhz2x6T2CgREiIiIiIiKifmLNmjW49NJLsXPnzvR9DQ0NuOaaazBz5kysXr0af//73/Hmm2/iL3/5CwBg1apVWLJkCe655x68//77uPjii3HjjTciFov11MvoVgyMEBEREREREfUDS5cuxa233opbbrkl4/5//OMfGDlyJG644QZomoZhw4bh0Ucfxfnnnw8AePbZZ3HhhRdi2rRp0DQN11xzDfLy8rBs2bKeeBndjoERIiIiIiIion5g9uzZeO2113DBBRdk3L9u3TqMHTsWP/rRjzBr1iycc845eOmllzB48GAAQGVlJcaOHZvxnNLSUpSXl3fbtvcktac3oC9wHAc1NTUIBAKQJKmnN4eIiAhCCEQiERQVFUGW+T3H0cDzPRER9SZCCDQ1NSEnJyfjXK/rOnRd7/A5hYWFHd7f3NyM119/HXfddRfuuOMObNmyBV/72teg6zq+8pWvIBKJwOfzZTzH6/UiGo0evRfUizEw0gU1NTWYM2dOT28GERFRO2+99Vb62x46MjzfExFRX3DTTTdh8eLFh/QcXddx4oknYuHChQCA8ePH48orr8S//vUvfOUrX4HP50M8Hs94TjweR15e3lHb7t6MgZEuCAQCANwPn8FgsIe3hoiICAiHw5gzZ076HEVHjud7IiLqTVLn+jfffBNZWVnp+zvLFjmQMWPGZEyoAdxMSSEEAKCsrAwVFRUZyysrK3HGGWccxpb3PQyMdEEqnTYYDPKDEhER9Sos+Th6eL4nIqLeKCsr64jPS5dccgmefPJJPPLII/jyl7+MyspKPPXUU7juuusAAAsXLsSiRYtw/vnnY9q0aXj66adRX1+PefPmHY2X0OsxMEJERERERETUj40ZMwZPPfUUfvGLX+Dhhx+G1+vF5ZdfjquuugoAcNppp+HOO+/EXXfdherqapSWluKRRx5Bbm5uz254N2FghIiIiIiIiKif2bRpU8btk046CU8//XSnj58/fz7mz59/rDerV2IbeyIiIiIiIiIasBgYISIiIiIiIqIBi6U0RF3gOALb6yMIxS1keVWMzA9AltnwkIiIiIiIqK9jYIToID7Z04zn1+5GZU0YCdOBR5NRWhTEJVOHYeLQnJ7ePCIiIiIiIjoCDIwQHcAne5px7/IKNEQMlOT44MtREDNsrN/djD2NMdw8t4zBESIiIiIioj6MPUaIOuE4As+v3Y2GiIHSoiCCXhWKLCHoVVFaFERDxMALa/fAcURPbyoREREREREdJgZGiDqxvT6CypowSnJ8kKTMfiKSJKEkx4eKmhC210d6aAuJiIiIiIjoSDEwQtSJUNxCwnTg05UOl/t0BQnTQShudfOWERERERER0dHCwAhRJ7K8KjyajJhhd7g8ZtjwaDKyvGzVQ0RERERE1Ff1ysBIQ0MD5s2bh1WrVqXvKy8vx9VXX40pU6Zg5syZuPvuu2FZ+7+pX7p0KebNm4fJkydjwYIF+PDDD9PLbNvGz3/+c8ycORNTpkzBjTfeiJqamm59TdT3jMwPoLQoiKrmGITI7CMihEBVcwxlRVkYmR/ooS0kIiIiIiKiI9XrAiNr1qzBpZdeip07d6bva2howDXXXIOZM2di9erV+Pvf/44333wTf/nLXwAAq1atwpIlS3DPPffg/fffx8UXX4wbb7wRsVgMAPDggw9i5cqVeP755/HOO+/A6/Xihz/8YY+8Puo7ZFnCJVOHYVBAR2VNGOG4BdsRCMctVNaEMSigY8HUoZBl6eArIyIiIiIiol6pVwVGli5diltvvRW33HJLxv3/+Mc/MHLkSNxwww3QNA3Dhg3Do48+ivPPPx8A8Oyzz+LCCy/EtGnToGkarrnmGuTl5WHZsmXp5ddffz1KSkoQDAbxgx/8AG+//TZ27drV7a+R+paJQ3Nw89wynDgsB00xA9vrImiKGZg0LJejeomIiIiIiPqBXtUcYfbs2bjooougqmpGcGTdunUYO3YsfvSjH2H58uXw+Xy45JJLcMMNNwAAKisrcckll2Ssq7S0FOXl5QiFQti3bx/Gjh2bXlZQUICcnBxs2rQJw4cP754XR33WxKE5mFCSje31EYTiFrK8KkbmB5gpQkRERERE1A/0qoyRwsJCqGr7WE1zczNeeOEFTJo0CW+++Sb+8Ic/4JlnnsFjjz0GAIhEIvD5fBnP8Xq9iEajiETcUap+v7/d8tQyooORZQmjC4M4aXguRhcGGRQhIiIiIiLqJ3pVYKQzuq7jxBNPxMKFC6FpGsaPH48rr7wS//rXvwAAPp8P8Xg84znxeByBQCAdMEn1G2m7nIiIiIiIiIgGrj4RGBkzZgwMw8i4z3Gc9KSQsrIyVFRUZCyvrKxEWVkZcnJyUFxcjMrKyvSy2tpaNDU1ZZTXEBEREREREdHA0ycCI5dccgk2b96MRx55BLZtY9OmTXjqqacwf/58AMDChQvx8ssv47333oNpmnj88cdRX1+PefPmAQAWLFiABx98ELt27UI4HMbPfvYzTJ8+HSNGjOjJl0VEREREREREPaxXNV/tzJgxY/DUU0/hF7/4BR5++GF4vV5cfvnluOqqqwAAp512Gu68807cddddqK6uRmlpKR555BHk5uYCABYtWgTLsnDFFVcgEolgxowZ+N3vftdzL4iIiIiIiIiIegVJpOpRqFPhcBjTpk3DmjVrEAwGe3pziIiIeG46BrhPiYioN+F5qfv0iVIaIiIiIiIiIqJjgYERIiIiIiIiIhqwGBghIiIiIiIiogGLgREiIiIiIiIiGrAYGCEiIiIiIiKiAYuBESIiIiIiIiIasBgYISIiIiIiIqIBi4ERIiIiIiIiIhqwGBghIiIiIiIiogGLgREiIiIiIiIiGrAYGCEiIiIiIiKiAYuBESIiIiIiIiIasBgYISIiIiIiIqIBi4ERIiIiIiIiIhqwGBghIiIiIiIiogGLgREiIiIiIiIiGrAYGCEiIiIiIiKiAYuBESIiIiIiIiIasBgYISIiIiIiIupHGhoaMG/ePKxatardspqaGsycORMvvPBCxv1Lly7FvHnzMHnyZCxYsAAffvhhd21uj2NghIiIiIiIiKifWLNmDS699FLs3Lmz3TLHcXDrrbeisbEx4/5Vq1ZhyZIluOeee/D+++/j4osvxo033ohYLNZdm92jGBghIiIiIiIi6geWLl2KW2+9FbfcckuHy++//34MHjwYJSUlGfc/++yzuPDCCzFt2jRomoZrrrkGeXl5WLZsWXdsdo9jYISIiIiIiIiolwqHwxk/hmF0+tjZs2fjtddewwUXXNBu2XvvvYdXXnkFd955Z7tllZWVGDt2bMZ9paWlKC8vP/IX0AeoPb0BRERERERERNSxOXPmZNy+6aabsHjx4g4fW1hY2OH99fX1+P73v497770XgUCg3fJIJAKfz5dxn9frRTQaPcyt7lsYGCEiIiIiIiLqpd566y0Eg8H0bV3XD+n5QgjcfvvtuOqqqzBx4sQOH+Pz+RCPxzPui8fjyMvLO/QN7oMYGCEiIiIiIiLqpYLBYEZg5FBVVVVh9erV+Pjjj3H//fcDcMtzfvzjH+Pf//43HnroIZSVlaGioiLjeZWVlTjjjDOOaNv7CgZGiIiIiIiIiPqpIUOGYP369Rn3nX322bjpppuwYMECAMDChQuxaNEinH/++Zg2bRqefvpp1NfXY968eT2xyd2OgREiIiIiIiKiAey0007DnXfeibvuugvV1dUoLS3FI488gtzc3J7etG7BwAgRERERERHREUpYNhKmDVmWEPRoPb052LRpU6fLVqxY0e6++fPnY/78+cdyk3otBkaIiIiIiIiIDpHtCMQtG3HDRsy0YdkOBIBcnw54enrr6FAwMEJERERERETUBQnTRsKyETMdxE0bjhAQoqe3io4UAyNEREREREREHegwK0QAjIX0LwyMEBERERERESUlTBtx00bMcpBgVsiAIPf0BnSkoaEB8+bNw6pVq9L33XnnnZg4cSKmTJmS/nnmmWfSy5cuXYp58+Zh8uTJWLBgAT788MP0Mtu28fOf/xwzZ87ElClTcOONN6KmpqZbXxMRERERERH1PrYjEDEs1IcT2N0YRVVzDA0RA9GEBdthUGQg6HWBkTVr1uDSSy/Fzp07M+5fv349lixZgg8//DD9c+mllwIAVq1ahSVLluCee+7B+++/j4svvhg33ngjYrEYAODBBx/EypUr8fzzz+Odd96B1+vFD3/4w25/bURERERERNTzEqaN5qiBfS1x7G6MoqYljuaYCcNy4LBUZsDpVYGRpUuX4tZbb8Utt9yScb9hGNi8eTMmTpzY4fOeffZZXHjhhZg2bRo0TcM111yDvLw8LFu2LL38+uuvR0lJCYLBIH7wgx/g7bffxq5du475ayIiIiIiIqKeZTsC4YTJrBDqUK8KjMyePRuvvfYaLrjggoz7y8vLYVkW7r33XsycORPnnXceHn74YTiOAwCorKzE2LFjM55TWlqK8vJyhEIh7Nu3L2N5QUEBcnJyDjjXmYiIiIiIiPqutlkhtaEEs0KoQ72q+WphYWGH94dCIUyfPh1XXXUVfvOb32Djxo1YtGgRZFnGddddh0gkAp/Pl/Ecr9eLaDSKSCQCAPD7/e2Wp5YRERERERFR32Y7AjHTQsJ0OEGGDkmvCox0ZtasWZg1a1b69qRJk3D11Vdj2bJluO666+Dz+RCPxzOeE4/HkZeXlw6YpPqNtF4eCASO/cYTERERERHRMZEw3TG6MdOGYTpwwLIYOnS9qpSmM6+//jr+9re/ZdxnGAa8Xi8AoKysDBUVFRnLKysrUVZWhpycHBQXF6OysjK9rLa2Fk1NTe3Kb4iIiIiIiKj3smwn3StkV4PbK6QxYiBm2LA5VpcOU58IjAghcPfdd+Pdd9+FEAIffvghnnjiifRUmoULF+Lll1/Ge++9B9M08fjjj6O+vh7z5s0DACxYsAAPPvggdu3ahXA4jJ/97GeYPn06RowY0ZMviwA4jsDW2jA+3tWErbVhOA6PZETU89oemyzLOeJjFY93REREh04IgbhhoylqoKo5hj2NsXSvENNmrxA6OvpEKc28efPwve99D3fddReqq6tRUFCAxYsXY/78+QCA0047DXfeeWd6eWlpKR555BHk5uYCABYtWgTLsnDFFVcgEolgxowZ+N3vftdzL4gAAJ/sacbza3ejsiaMhOnAo8koLQrikqnDMHFoTk9vHhENUG2PTabtIGHZ8KgKNEU+rGMVj3dERERdlzr3xk3HzQRx3AAI0bEiCcFko4MJh8OYNm0a1qxZg2Aw2NOb0y98sqcZ9y6vQEPEQEmODz5dQcywUdUcw6CAjpvnlvFigYi6XdtjU9yysW53MyJxE0GPiknDcuHRlEM6Vh2r4x3PTUcf9ykRUc8QQqQbpsZMG6blwBGiz2aC5Pl15AX0I14Pz0vdp0+U0lD/4jgCz6/djYaIgdKiIIJeFYosIehVUVoUREPEwAtr9zDNnIi6VdtjU8CjYEd9BLbjoCjbA0sI7GiIIuhRunys4vGOiIioY6btIBQ3URtKYFdjDPtaYmiMGoibyV4hPb2BNKAwMELdbnt9BJU1YZTk+CBJUsYySZJQkuNDRU0I2+s5TpmIuk/bY1MobqElZsGvq5AkGX5dRXPMRChhdflYxeMdERGRSwiBmGGhIWJgb5PbK6QulEAobsKyWSpDPatP9Bih/iUUd2eL+3KUDpf7dAXVLQ5Ccaubt4yIBrK2xybTdmA7AqrsBjRUWULMETAtB0DXjlU83hER0UBmWI47TjfZL8Tpx71ChBCobkmgfF8LCrO8+OykknZfilDvxcAIdbssrwqPJiNm2Ah6278FY4YNjyYjq4NlRETHSttjk6bIUGQJliOgKe7/K7IETXWTLbtyrOLxjoiIBhLHEYhbNuKG2y/EtG2Ifjo1JmHa2FwdxqdVLdiwtwUbqlrQEDHSy8MJC5dP5xTUvoKfxKjbjcwPoLQoiPW7m1HqCWZEUoUQqGqOYdKwXIzMD/TgVhLRQNP22JTlVZHtU9EQMZDtVRE1LOQHPMjyqF0+VvF4R0RE/V3Cst2sENNBwnJgOw7623iPVDbIp8kAyIa9LaisDcM+QPpLdUu8G7eQjhQDI9TtZFnCJVOHYU9jLF1733ZKw4KpQyHLTD0jou7T0bHpuPwAmmMWaloSCHpUjBjkRzjR9WMVj3dERNTf2OmsELc8pj9mhRwsG+RAhub6cHpZAa6dNeoYbyUdTQyMUI+YODQHN88tw/Nrd6OyJozqFgceTcakYblYMHUoR/USUY9oe2xKmA6G5HiRCOjwqDKaYybiln1Ixyoe74iIqK9LmDbipo245SBu2u4o3X4SCRFCoDqUcAMge1vwaVULKmsOnA2S4lVljC/JwoSSbEwYko0JJdnI9evI8+vI8WndsPV0tDAwQj1m4tAcTCjJxvb6CEJxC1leFSPzA/zmlIh6VEfHphF5fuxsjB72sYrHOyIi6ktsRyBmug3EY6YNy3b6TVZI22yQjVUtqO9iNsiQXC8mlGTjhGQQZHRhEArP5f0CAyPUo2RZwujCYE9vBhFRho6OTUd6rOLxjoiIerNUVkgsOUmmP2SFdJQNsqUmDOsQs0GOT2aE5Pn1bthq6gkMjBAREREREQ0w/TErJGHaqKgJZzRJZTYIdQUDI0RERERERP2cEAKG5QZBYqYNw3TgoO9mhRxpNsi4wVnpviDMBiEGRoiIiIiIiPohy3bcCTKmg5hhp0fp9sVYiGE52FwdOqJskFQQZAyzQagNBkaIiIiIiIj6ASFEujQmbvXdrBAhBGqS2SCpJqmVzAahY4iBESIiIiIioj7KtN0Ruglrf1ZIF+IHvcqRZIOU5HjTfUGYDUKHi4ERIiIiIiKiPkIIgbiZLI8xbZiW406Q6ekN66IjyQbxpLJBkk1Sjy/JxqAAs0HoyDEwQkRERERE1IsZloOEtT8Y4vShrJBUNkgqE+TTqhbUhw8vG2R0QQCqIh/jLaaBiIERIiIiIiKiXiSVFRIz3TIZw3Ig+kBWSNtskI1VLaioZjYI9X4MjBAREREREfUww3KQMG3EkpkhfSEr5EizQVKZICcwG4R6GAMjRERERERE3awvZoXUtMSxoaol3SS1siYM0z60bJBUMITZINSbMDBCRERERETUDVJZIXGr9/cKSWWDbKza3yS1jtkg1E8xMEJERERERHQM9KWskCPJBhlbnJXRJJXZINTXMDBCRERERER0lJi2kzFOtzdmhRiWg4qaUMbI3EPJBjk+WRJzwpBsjClkNgj1fQyMEBERERERHaa+kBVSG0okM0GasWFvCyq6mA2iqzLGMRukSyQAkgRIktTTm0KHgYERIiIiIiKiQ9Cbs0JaZ4NsqHL/vzac6NJz2Rvk4NIBEEhQFAmaIkOTZWiqBE2WoSpSr9hnDQ0NuPTSS/G///u/mDFjBgDg3//+Nx544AHs2rULubm5WLBgAb7+9a9Dlt3tXbp0KR544AHU1tZi9OjRuOOOOzBlypSefBndhoERIiIiIiKiAxBCIJEMgsRMG6blwOklWSHsDXJstA6AqIob+FBlGZoiJQMhMhS5d2aHrFmzBt/97nexc+fO9H2ffPIJbr/9dvzud7/DnDlzsG3bNlx//fXw+/348pe/jFWrVmHJkiV45JFHMGnSJDz99NO48cYb8cYbb8Dn8/Xgq+keDIwQERERERG10TorJG7asHtBVsiR9gZhNkh7bvDDLYFRW2V+7M8A6b0BkI4sXboU9957L2677Tbccsst6fv37NmDyy67DGeddRYAYMyYMZg3bx7ef/99fPnLX8azzz6LCy+8ENOmTQMAXHPNNXjmmWewbNkyXHLJJT3yWroTAyNERERERDTg9caskFRvkE/3NjMb5Ai1DoBoigxNkaHK0v5AiCJD7qUBkHA4nHFb13Xoesd/z9mzZ+Oiiy6CqqoZgZHzzjsP5513Xvp2PB7Hm2++iYsuuggAUFlZ2S4AUlpaivLy8qP1Mno1BkaIiIiIiGhAMm0HCSvZK8To2ayQI8kGGZztxYQhnBQDtA+A6KobAFGT/UB0Re5zDVLnzJmTcfumm27C4sWLO3xsYWHhQdcXDofxjW98A16vF9dccw0AIBKJtCuZ8Xq9iEajh7fRfQwDI0RERERENCD0pqyQozEp5vhkIGSgZYNIyf+RAMiyG/jQVRmaLEFNBkL6YgCkM2+99RaCwWD6dmfZIl2xdetW3HzzzcjPz8cTTzyRXq/P50M8Hs94bDweR15e3mH/rr6EgREiIiIiIuq3ekOvkHQ2SHJKzKFMihnI2SAdBUA8qQyQfhgA6UwwGMwIjByut956C9/61rfwxS9+Ed/+9rehqvvDAWVlZaioqMh4fGVlJc4444wj/r19AQMjRERERETUb/SGrJAjywYJJvuC5AyYbJCDBUC05DSY/h4AOZY++ugjLFq0CHfddRcWLlzYbvnChQuxaNEinH/++Zg2bRqefvpp1NfXY968eT2wtd2vVwZGOpq5nFJTU4PPfe5zuPXWW7FgwYL0/QeauWzbNn71q1/hxRdfRCwWw6mnnoof//jHKCoq6tbXRURERNSfOY7A9voIQnELWV4VI/MDvaqZYW/fPjp8rbNCYqYNpxuzQgzLwaZ9Iby2oRoVNWHsa4mjOWZ26bmDs704viTZJHVINsYUBqEdJBvEtgXW7mxEQ8TAoICOqSPyoCh9433cNgCiKW7GR08FQFofEwIeBQAQSdiHfXzozceYP/7xj7AsCz/96U/x05/+NH3/tGnT8Kc//QmnnXYa7rzzTtx1112orq5GaWkpHnnkEeTm5vbcRnejXhcY6WjmcorjOLj11lvR2NiYcf/BZi4/+OCDWLlyJZ5//nlkZWXhjjvuwA9/+EM8/PDD3fWyiIiIiPq1T/Y04/m1u1FZE0bCdODRZJQWBXHJ1GGYODSnpzev128fHRohBOKmjViyPMawHIhuygqpDSWwIdkc9dO9LdhUHYLdhShM22yQCSVZyA96Dul3L99Yjb+9vws1oQRsW0BRJBRleXDZKcMx9/jiw31JR53b/NT9L1mWMgIgmiq7Y3F7OAOk9TGhIWygMeY2us3z6xgU0A/5+NAbjzGbNm1K//cf//jHgz5+/vz5mD9//rHcpF6rVwVGOpu5nHL//fdj8ODBKCkpybj/YDOXn332Wdx6663p5/3gBz/A7NmzsWvXLgwfPvzYvzAiIiKifuyTPc24d3kFGiIGSnJ88OUoiBk21u9uxp7GGG6eW9ajwYfevn3UNYblIGHaiFvdlxViWA4qa8LpKTGH0htEloDxg7Nw9viiLmeDHMjyjdV44M0tSJg2gh4VuleGYTmoaorhgTe3AEC3B0dSARAJEpTk1JdU1oeqSFCTY3F7m9bHBL+uojFmIG7YEAAkYSA/oB/S8YHHmL6vVwVGOpu5DADvvfceXnnlFTz//PPpWcspB5q5HAqFsG/fPowdOza9rKCgADk5Odi0aRMDI0RERERHwHEEnl+7Gw0RA6VFwfQ3wEGvilJPEJU1Ybywdg8mlGT3SEp5b98+6lxPZIW0zQapqAl1qTcIAOiKDI/qjofVVQnNUROG5WD+SUOPuNTFtgX+9v4uJEwbg4I6JLjBBo8mQ9dkNIQNPPP+Lpw5tuiYlNWkAyCS5GZ7qO7/66oETZahKjKUPvLvp/UxYUxhAB/uaoJhOcj165AkoDlmorolgSnDc1BZGzno8YHHmP6hVwVGOpu5XF9fj+9///u49957EQgE2i0/0MzlSCQCAPD7/e2Wp5YRERER0eHZXh9BZU0YJTm+dmnxkiShJMeHipoQttdHMLrwyKcq9Lfto0zd2SvEtJPZIMlMkA1VLagJdS0bpDjbg8HZXpTvC8GvyQh4tHbvr6BHoDqUwNqdjThl1KAj2ta1OxtRE0og6FHTQZEUCTKCHvWo/C5JSgVBktkf6fIXKZkJIvf5i/vWx4RwwkZLzIJfV9N/P7+uojlmImzYXTo+8BjTP/SqwEhHhBC4/fbbcdVVV2HixIkdPuZAM5dTAZNYLNZueUdBFiIiIiLqulDcQsJ04MtROlzu0xVUtzgIxa1u3jJXb9++ga47s0IONxtEUySMK85yR+YOycYJJdnID3rw70/2oaI6jIBH7bBXhqbKCCdsNESMI972hogB2xbQvR2XpRzq7+ooAJIqf+kvAZDOtD4mNEUN2I6A2uq1qrKEmCNgWg5y/PpBjw88xvQPvT4wUlVVhdWrV+Pjjz/G/fffDwAIh8P48Y9/jH//+9946KGHDjhzOScnB8XFxaisrEyX09TW1qKpqSmjvIaIiIiIDl2WV4VHkxEzbAS97T9axgwbHk1GVgfLukNv376ByLIdxC0bMePYZYUcSTZIUZYnPSVmQkk2Sos67g0yKKBDUSQYlttos902WA4URToq43YP53cdbAKMKru3B9oI3NbHBC1ZAmQ5AlqyBMlyBJRkk9iuHB94jOkfev1fZ8iQIVi/fn3GfWeffTZuuumm9Ljeg81cXrBgAR588EGceOKJyMvLw89+9jNMnz4dI0aM6PbXQ0RERNSfjMwPoLQoiPW7m1HqCWZcZAkhUNUcw6RhuRiZ3zOZur19+waKhGkjlvwxTAfOUc4KqQsn0pkgG6pasLn68LJBJpRko6CLk2KmjshDUZYHVU0x6JqcUeIi4CCcsDAk14epI/IO+3Ud7HcpkgRFETAsYFxxEDNGDYLXo0JLToJRFTnZC6T3NUDtKa2PCWMKA8j2qWiIGMj2apAkIGpYyA94ENQVVNZGDnp84DGmf+j1gZGuONjM5UWLFsGyLFxxxRWIRCKYMWMGfve73/XoNhMRERH1B7Is4ZKpw7CnMZaus/fp7kSGquYYBgV0LJg6tMfS8nv79vVXtiMQM90Sg5hpw7IdCIGjEgxpnQ2yscoNhhztbJCuUBQJl50yHA+8uQUNYQNBjwpNlWFablDEoym49JThR6UZqqJIuHLGCDz+3+0QQiDHp8KvK7AFEE2YyM714Sunj8ZI9rA4qNbHhC21EQzO9iGcsNAUNSAA+DUFxdkeVNZGunR84DGmf5CEEN0x7rtPC4fDmDZtGtasWYNgkAcbIiLqeTw3HX3cp0fmkz3NeH7tblTWhJEw3XT/sqIsLJg6tFeMqezt29cfJEzb7ReSHKvrCIGjcaVxJNkgY4uzMKEkOx0M6Wo2yKFYvrEaf3t/F2pCCdi2gKJIKM7y4NJThh/W+NzW/T9SE2C05P+/vbkOf1u9A3ua4oglM2+Kgh585fRRmD956FF/bf1Z62NCQ9hAY8ztz5Ln1zEooB/y8eFYHGN4Xuo+/SJjhIiIiIh61sShOZhQko3t9RGE4hayvCpG5gd6zbekvX37+iLbEYhbNuKGfdSyQlLZIK2bpB5qNsjxyUDIkWSDHIq5xxfjzLFFWLuzEQ0RA4MCOqaOyDtgpkjr/h+HMgHmghNLcO7xxVi5pQ61oQQKszyYNaYAKktlDlnbY0LA4zZPjSTswzo+8BjTtzEwQkRERERHhSxLvXocZW/fvr7gaGeF1IcT+DQZBNmwtwWba8IwLOegz+uubJCuUhSpwzG5+wMgEuRk348jbYCqqjLmjCs6ehs/gB3tYwKPMX0XAyNERERERNSho5kV0jYbZENVC6pbup4NMqEkOS53SDbGFAZ7VUNRN/OjVQBElaElgx5qsglqd2SvENHhYWCEiIiIiIjSjlZWyJFkg5QVZWU0SS3M6rlskNZaB0AURYKuulNfNMUtf0kFQYiobzlmgZF4PA6v13usVk9ERETUL1iW02m/gNbLsv0KyveGsKcpjuF5flw3axS8XhWGYeOvH+zA//ukGgnLxiC/jpH5XmysisC2TKzdE4JlAz5NwoUnDMK/NjQiajhQ4V7gxZMXvN5W/93WKC+wLd7xsrkjgOU7O172uROAf5UDCbvr+2NyNhD3BrAvlIBfdtAScxBudS1dmquixQJqwlbG874yxYsGkYvK2ihUJ4EPq9xMBAnA6cNVfFwjYNg2DAtovTmLZ+UjJmchEbfw7pZqbG80IclAWb4XIwuD2NucwOY9IURb7ZvfXjYeud4g6sMGsv0qPtrWgH98vBemLTBhcACThuWhOmxACAdvlNcgYgjkB3U8d8MMFOUEDtiDIBo18YtXy/Hx7mYEPQquOm0kzh5fnH5POI5o1xMhFLdQ0xzHS+v2YG9THENzvbh+9mg0xk3UtCQgBDCmMICIYSPbpyHL614CRBIWakIxLF27Fy1xC0OyvfjyrJGoj5oIJSzoqoSalgQaI2ZG34xozMS9b1SiqiWOkmwvFs0Zg0/2tWDTvhAaoyb2tcSwYW8IEaNrf/iiLA+OH5wNv0dGY9iAkARG5gcxJMeLcNzEW5ur8U5FLZqiNobn+fDd88Yh6NcBAKbp4LWN1agOxVGc5cXccUWoiSTQEjexoy4KSQIKgp52PT8cIbCnMYaIYcOnytjXEkdT1ESWT0FzxERDzERJthdnjClAZX0EkYSF/KAHU4bnoj5iIGZayPZqKMjxHHIPidZ/wwP1oejq44joyB3WVJq5c+fi+9//PubOndvh8pdeegk//elPsWrVqiPewN6A3YCJiKi34bnp6OuJffriR3vw0FtbsLc5DssWUBUJQ3K8uGHOGABILwvHTbT9ot2jSji+JBsb9rbA6MKEDjr6FAk41F2vKcCpowvSUytKi4K4ZOowTByag0X/twavrNvX7jlZXgX/+7kTMaYwuH+KRsRAY9SAaTloihkwO4hBSAAUWYIQApIEBHQVuX4NAV2BR1PQHLNgOg5ihu3+mDYcAfh1BYMCOurCCdiOgCK7zUGLsjywHYHt9dHD22Ed0BXgqtNG4oW1e9AYNbv8vPGDg5hVWoDnPtiNcMKCI9xAnyIDQY+GqGHBsgUkAB5VweBcLy5LTompqA7j3xv2YU9jDI1RA6G4CUUGFEmGLO/P9rAdB6bl7jsBwHQcSA4wKEtHjldv9/frio4ml3S0jq4+jvo3nuu7T5cyRhoaGrBly5b07T179mD9+vXIzs5u91jHcbB8+XIkEl2rFyQiIiIaiF78aA/+958bEEt+i+/xyUhYDnbWR/HDf3wCABCOgCPQLigiAUhYAh/tau7+Dae0w4lHmTbw7pY6LJg6HDHDxvrdzdjTGEPUtPCfivoOnxOK27j17x9j/OAseDQFfl1BU8RAKGEhFLc6fA7gXsyrioSArsGnK9AUGZIQaIqZCDfFETPtDsfeRg0bUSMGWXKDP44jAFnCltrIob/ggzBs4M//2X7IzyvfF0b5vjAkAJoqQQVgWAKmjXSARVcl998QBOrDCTz13g6EEhZ21EcQNxzk+TU0hhPQFfffXtS2YVgmTNuBkeylkhL0KLBsAcN20Bw3MfU4Hbk+Pf33u3lu2UEDFp/saca9yyvQEDFQkuODL0fJeA+k1tHVxxHR0dOlwIiu6/jGN76BxsZGAO5IqYceeggPPfRQh48XQuCMM844eltJRERE1I9YloOH3tqCmGGjKNsDSXK/pfbpMnRFwo6GGCQJGJ7jwY6m9l82MT+kb7McIJEwEPR5UOoJYvO+ENbuajrgc0xHoKK6BRefNAQf7WlB3LIB0b5fh6ZI8OkKfJr7I0kSLMdBwrBRHzYQ6yi1pBMy3OCP4wCGfQj1UN1IlwFVlmFYTrrRqaa6jU5zvAo0VYFjC2gqYFoCr32yD0PyfBiW68P/21CNxogBXZERPkjZTzhhw6NK8OsK4qaDTftCGDkhgNKiICprwnhh7R5MKMnutNTFcQSeX7sbDREDpUXB9BSaoFdFqWf/OsYXZ3XpcQf6XUR06LoUGAkGg/jFL36Bjz76CEII3H///Zg3bx7GjRvX7rGyLKOgoAAXXHDBUd9YIiIiov5g5ZY67G2OI9unpYMiKYnkN/hCAPXRzrMBqG97vbwOF08ZCkmS0Bg1uvSchA1sb4yhJWZBVxU0RU1IEuBV3SySVFaIEAJx00FD1EDccLMfDqfsx+qFEThJAvTkhBdNkeBVZQQ8GgREelqO7QiYtoOY6f4kLAdx04GuSmiJWcj26djREENj1ISqyLCczl+ohP2BSCEAWXIbrkYMG/ta4ijJ9aEkx4eKmhC210c6HdW6vT6CypowSnJ87UbzSpKUXsfKLXVdetyBfhcRHbouN1+dPXs2Zs+eDQBYvXo1/ud//gennXbaMdswIiIiov6qNpSAZQt4fO2nV9ipwAjcLAHqn+KtMjASVtezMaIJG7IEZHlVCOGFV5MhSZIbCDBs1BsG4pbdbopMX3snuX1NpFZBENktmWnVA8S0HVi2g5hpIRy3YNhuECT1z0ZXJHg1BQKAZTvwqoo7YQcCcdOG4wgoigyriy0XUw+TJQmO4yCezL7x6QqqW5wDljWF4hYSpgNfjtLh8tQ6akOJLj3uQL+LiA7dYU2lefLJJ4/2dhARERENGIVZHqiKhITlwKdnBkdSkzMkAJosgV3b+ievsv/C16N2fBGcIkmAP9lbZES+z73YFwItMRMNUQMxo+NeIa0dzrhdwA0uSJCQsA8+ZvdwqMnGrprqBkF0RYamypCT2RJCCFiOgGE5CMfd12lYDkzH7QGiSIBXU5PBIIHWscRUqYkQbraFAzeoIcENmMiyBFsIdLUiJZXA4QgBWXbXAQAxw4ZHk9PTfjqS5VXh0WTEDBvBDh6XWkdhlqdLjzvQ7yKiQ3fY/6IaGhrw9ttvo6amBobRPv1PkiQsWrToiDaOiIiIqD+aNaYAQ3K82FkfTX7jvz844kkFRiQg368etPcB9U3njC8A4F745/l1bGsz6UVXZPh0NxiSugCH4yCgKgjFTVS3xGFaNiJmF7MdDmMbfapbJm/aDvyahGgXf1dH9GTpS6r/R+p2qlzEEQKm5cC0BSKG2wDV/Tnw79RlQJYBRZLaZX5ospTOFtFVCXHDRrZXhS0cFGf54dcVhOMWvGr7zK2U1muUJHc7DctBllfF4GwvhBCoao5h0rBcjMwPdLqekfluP5L1u5tR6glmlMm0XsesMQV4c3PtQR93oN9FRIfusAIjn376Ka699lqEQiF0Nu2XgREiIiKijqmqjBvmjMH//nMDaloSyPZp0FW3gWRLzEx/U1wfteDXFETbNMxs3feA+h5VBjweHeG4harmGAqyPDh9bD7Wbm+CX1fh1WWosgxHCMQMG3WhBEzLxoh8PzZWh5Hl1dAQTiAuju07IWEDimNDliV4NBUx0zzgb2vb/0NTZOjJQEhKqv9HwrIRSrjjcA3bgX2YZWMJBxC2A0UGWlckSXBL0YTjAJIE0xYIelRcM2skPt7VjC11EYwuCODTPc2ImW550oE2ITWVJmrYUGUJ44qzEDFsVDXHMCigY8HUoQdshirLEi6ZOgx7GmPpHiI+3Z0203odqip36XFsvEp0dB1WYOTXv/41YrEYvv71r+Okk06C1+s92ttFRERE1K/NnzwUAPDQW1uwtzmOlrgFVZFwXH4AX50zOmOZYdsZI3sFAI/qPraiOswgSQ85nIammgKcOroA2+siCHoVTB81CBedVILj8gP46Ssb8NbmOkQSNqJGAnHT/aMHdBk3nDkGhiWwvLwa2+ujiCSsQ/q7S3DLVryaDDUZqGiKdhzo8OsKBgV01IUTsB2RnGwjMKYwAMsR2NkQTWd9pAIfXtWdhmPYbjlLKuMjatgwLRNG8nbb4INPk/GteWV4fOV21Ia71oQWACYPz8G8CcX48zvb0BK34AgBSQY8soQsr4ZIwoJpC0gAvKqMEYP8+Oqc0Zg/eSg+2dOM59fuRmVNGMMG+bGvOQ7TceA4Iv3vTML+0hldkQFIUJNTaQqzPXAE0BQzMGlYLhZMHdql8bkTh+bg5rll6d9d3eLAo8nt1tHVxxHR0SOJzlI+DmDatGn44he/iO985zvHYpt6nXA4jGnTpmHNmjUIBtn9mYiIeh7PTUdfT+1Ty3KwcksdakMJFGZ5MGtMAdRkan/rZdl+BeV7Q9jTFMfwPD++fNpI/HL5Zqzb1QjHcbCnKQ7bEfBoCnyyQHXEgld20GK636T7NAkXnjAI/9rQiKjhQIV74RdPfhL0tvrvtnwAYt2yNw7fyCAQEhoAYHiWhI/2uRfZEoDTh6v4uEbAsG0YFtA6/+ammYPwxnYTtS0JxAwTYUNAkoBsj4KgV0Vj1ETCcNC61eVvLxuPXG8Q9WED2X4VH21rwD8+3gvTFpgwOIBJw/JQHTYghIM3ymsQMQTygzqeu2EGinIC2NUQRUvchCpLyPXrMB0BO9kzIx638NA7W/Dx7mbYjkBeQEdNyMC+lniX9oMqS8jzazijrABF2V54VRmKLGPEIB9ipoOAV0XQ45bmhBM2akIxLF27F01RE8PzfPjOuePQlLAQMWx4NQn1IQMtMQv5QR0nDcuF16PANm08+t8dqA7FUZztxTfOGoNP9oVQ25JAXlBDflDH//v/7P15nBx3feePPz919TndPZek0YxkW4cPWRayxeFD4IDxksQ4BhviJLCEDQssGMyXxMmywYSwmCQs5LfGWQ4bTICEBHCM4mAIEIIxRiHG1uFLlqx7DmlGc/V91PX5/VHdrZ770Iw0kj5PHsIzXV1Vn6ru6a56f17v1+u5ATJlh4vbmvCFz0v9OQYyBXb15ig7Pu3xEJ95yxVsubANTRO4rs9jLw3w4+cHGCkGUbWrm6PouqBgO/zo+X6GCy5r2mL89W2bSMRDANi2x7bdffSlS3SmItyyaSXHcmVGizYHBvIIAcsT4TF/VxDE5x4ZLpArB6qs3nSR4bxNKmZwIluhP1OhMxXh5o0dPNUzWv/7vOaiVnozJXJll6awwYWtsTmrNxr3Pd02Zvs8xbmL+q4/fcy7MPKRj3yEt771rYsxpiWHekMqFAqFYqmhvpsWnrPtnB4azPPxf3mBVMSa1KQxX3ZJl2w+8VuXzyvWs3H7EsnTR0br6oBCxeVEtowELEOjUp1mr82yT9aSoBG0E3i+nKBSMDSBEIFJputLNMCvbi9q6ejVm0HHk1RcD0MXgODaNa10pCKndMyLfR5ruJ5P2fWoOD4lx8P1/Hq87EjBZs+xLHuOZ3nhWJaXBnL1czodhiZYvzzOho4El69MsKEjwbLE/JTcovp/AoE2JhEmaIkx9LEtMQqFYvE5276Xzmbm1Urz8pe/nF/96lfnTWFEoVAoFAqFYqkx2/jP+cZ6Nm4/U7TxfIlRLVA0Fjf8+cadNCCRCGYzEy6REjSC1o7yOO+V+RzzYp1Hz5dBIcQOCiKO6+MTGIweGirwwrFsvRhyPDM7NUhr3OLyjgQbqkWQi5c3YU1jHDoZJwsggbFqYwHEaIjGVSgUivOJeRVG/vAP/5Df/d3f5f777+fNb34z7e3tYxyTFQqFQqFQKBSLy2zjP+cb69m4fdPQ0LWgGGHqAl0TdQPYIFb11IojsyuKVJ8pwGdsXGqN+RzzQp1H3w/ULGUnKITYro8vZV0N8kK1CLKv//SpQYSoqXgajFA1gWFomJo256KKQqFQnKvM6lvj0ksvnVD4kFJy7733cu+99066jhCCPXv2nPIAFQqFQqFQKBQTmW3853xjPcdsvz1GImIwUrBJRkzCRrX1BTA1qDSsV2ujmaxcIgT1lpmxSEAg5ck2mvqj4uQx+VKiawLHlSQjOisaCgXzPeZTOY8V16PieJQcn4rrYzsuBwZPvxqk1sJUK4CYdQWIqJukqklMhUKhmJpZFUZe8YpXLPY4FAqFQqFQKBRzYLbxn/M1axyz/cECKxJhCmWXkYKNIFBaFGyPouPX01ka6x26Dp538jEfpsxD9eTYZQIwdVGPqzUNDSmr7R/VZS2xEAXbO+Vjns15vK26Tc+XlBy37hMymC3z/DzVIOuWxdmwMlEvhixrCs1YvKgXQKi2vRgCU2v4r64pc06FQqGYB7MqjPzd3/3dYo9DoVAoFAqFQjFHFjvWc/z2UzELWQzSXpojFq7vM5irBKaono89JlNY0BwzuGZtK7u70xzPlMeoSaKWTiJsMFK0sd1ggRAQMXUu7WiiMxVh30COnpEithvEroYsnQuao/zmphUM5e0FO+bxxzmUlyQiBteubeXGDcvpbI7Qmy7y4rEsz/Vl6q0xi6UGaSyAaJqotsA0FkK0uiGtQqFQKE6d+TWdKhQKhUKhUCiWBBs7k2zoSCxarOf47ceqUa+FikdT2KArGeGXh4cZzFVojpj058oMZIOo0zdv7sSydFzX54n9g2w/OIRAcPXaFl69tp3eTInRos1L/TlGCjZRS2frunbWLgvSF44MF0gXbPafmBi7utBRprXjPDyUJ110yJUd+tJl/vFX3bxwLFCDlBdBDVLzAdE0DUMThIyTSTC1NBiFQqFQLC7zKoy84x3vmHa5EALLsmhra+Oqq67iTW96E6ZpzmuACoVCoVAoFIrp0TRxSlGyp7r96y9ZNu36hqHx2suW89rLlo95vLbNLRe0TLremvY4tMNVF05cvlDH7Ho++YrD831ZdnanebY3zZ7jWY6lZ6kGiVl1JcjlK6dWgzQaoRrauDYY5QOiUCgUZ5R5FUaOHz/O8PAwxWIRgKamJkKhEMPDw0gpESIwzwL453/+Z7797W/z93//94TD88tVVygUirOZhZ7VnA7X9dl+cIjBXIX2plB9ZlWhUCgUAb4fGKr+6vAIO7vTPNebZu881CAbqmqQ5ePUII1GqEY1+aVuhKp8QBQKxTlKLpfDdV2am5vP9FDmxbwKI5/5zGf4/d//fX77t3+bD3zgAyxbFswSZLNZHnzwQb7xjW/w4IMP0tXVxSOPPMK9997LAw88wJ133rmgg1coFIqlzvN9mXrPesUJ+uDXLYtz21Vdp9z7P55Hdvdx/+MHOZYp43oSQxesTIZ57/VruWVz54LuS6FQKM4WXM/nud4MTx0ZYWdPmud6M/SlS7Nad7waZP2yOKFqRHCjD4je0PpiKSNUhUJxHrF9+3Y+/elPs3//fgC6urq44447eNOb3nRmBzZH5lUY+eu//muuueYa/vf//t9jHk8kEnz4wx/myJEjfO5zn+PrX/867373uzl69Cj/+q//qgojCoXivOL5vgz3/ft+Rgp2kHKQDFIOnuvN0Dda4s4b1i9YceSR3X3c8+geSrZHImISimhUXJ/u4SL3PBpEp6viiEKhOB8Yzld46sgITx0ZZVf3KHuOZyk7M6tB9KoapG6SWlWDaEJMboRaLYYYulA+IAqF4rxk165dvPe978XzPNatW4eu6xw6dIj/9b/+F+Vymd/5nd8500OcNfMqjDz33HP8yZ/8yZTLr776av7qr/6q/vumTZt49NFH57MrhUKhOCvxfcnDO3sZKdisWxavy6zjYYN1oTgHTuT57s4+NnQkTnlG0XV97n/8ICXbY1kihBDBBXrE0gibGieyFR54/BA3bexQbTUKheKcwvV89vbn2HF0lKePjrCrO03v6OzUIC0xq94Oc3lHgkuWxwlZeqAA0QSmoWFpDW0wyghVoVAoxvDggw+SSCT4yle+woYNG4DAduOOO+7gC1/4wrlfGInH4xw9enTK5UeOHBnjJ1IsFonFYvPZlUKhUJyVHBkucOBEno5kZIKZnhCCjmSE/SdyHBkunLJ54PaDQxzLlElEzHpR5OS+NBIRk75Mie0Hh2Y0SFQoFIqlzHC+wq7uNDuOjrLj6AjP9WUpOd6M641Xg1y+MsGKRAit2vLSmARjVFtiFAqFQjE9zzzzDL/3e79XL4oAdHR08KEPfYj/8T/+Bz09PaxateoMjnD2zKsw8prXvIZ/+Id/4BWveAWvf/3rxyz71a9+xT/+4z9y4403AlAqlXjkkUe4+OKLT320CoVCcZaQK7tUHJ9IUp90ecTSGcj65MruKe9rMFfB9SShyOQX8pahkS27DOYqp7wvhUKhOF3U1CC7ukd5+ugoO7tH6RmZmxrk8moR5NLlTcTC5tgkmKoxqkqCUSgU5yIjIyPcfvvt3HPPPbzqVa8CgkLGPffcw4EDB2hubuZ973sfb33rW+vrbNu2jS984QsMDg6yZs0aPvaxj3HllVdOuY/R0VE6Oye2al966aVIKTlx4sS5XRj58Ic/zH/+53/ywQ9+kLVr17JmzRpM0+Tw4cO8+OKLtLW18Yd/+If4vs+rX/1qCoUCX/rSlxZ67AqFQrFkaQobhEyNku0RD0/8qC3ZHiFTo2mSZXOlvSmEoQsqrk/EmlgcsV0fQxe0N4VOeV8KhUKxWAznK+zsTrOre5QdR0d5tjczZzXI5Z0JNq5M0tUcIWzqWFX1h2lo6MoIVaFQnCfs2LGDj3zkI3R3d9cfy2QyvOc97+HOO+/k9ttv56mnnuKOO+7gkksuYdOmTTz55JN88pOf5Mtf/jKbNm3im9/8Ju973/t47LHHiEQik+7HdV0MY+K1rGVZANi2vTgHuAjM64q8vb2dhx9+mL/5m7/hhz/8IQcOHACC2N63vOUt3HnnnbS3tzM8PMyVV17JbbfdxvXXX7+gA1coFIqlzIWtMdYti/Ncb4Z1ofiYGUkpg6jITV0pLmw99TbD69a2sTIZpnu4SNjUxrTTSOmTLTlc0BrjurVtp7wvhUKhWAga1SA7jo6ysztN90hxVuvWkmIuX5lgY2eSjZ1JEiETy1QqEIVCodi2bRv33Xcff/zHf8yHP/zh+uM//vGPSaVSvO1tbwPgmmuu4eabb+ab3/wmmzZt4qGHHuKmm25iy5YtALzzne/k29/+Nj/4wQ+47bbbzsixnE7mPVXZ3NzMn/3Zn/Fnf/ZnZDIZHMehtbV1zBdRa2srX/7ylxdkoAqFQnE2oWmC267qom+0VPcaiVhBKs3xTImWmMWtV3UuSJSjYWi89/q13PPoHk5kKyQiJpahYbtBUSRi6bzn+jXKeFWhUJwxamqQnUdH2HF0lOf6MpRmmRSzflm83hLzslUpLmiJEqqqQVQRRKFQKMaydetWbr75ZgzDGFMY2b9//wR7i3Xr1vFP//RPABw4cGBCAWTdunXs3bt38Qe9BDh1DTeQTC5M3GSNyfqhvvnNb/L1r3+dwcFB2tvbecc73sHb3/72+jrT9UN5nsdnP/tZHnnkEUqlEldffTWf+MQnWLZMmRAqFIrFY2NnkjtvWM/DO3s5cCLPQNYnZGps6kpx61WdCxbVCyejeO9//CDHMmWyZRdDF1zQGuM9169RUb0KheK00ZgUs+NoEJnbM4ekmMur3iBXrEqyqTNFImwQMnTVDrMEcF2f7QeHGMxVaG8Kcd3aNlV0VyhOA/l8fszvlmXV21XG097ePunjhUJhQktMOBymWCzOavlUPP3003je2LbHQqEAwPbt2xkYGJiwzpve9KZpt3kmmFVh5IYbbuBP//RPueGGG+q/z4QQgp/85CdzHtBk/VA//elP+dznPsdXv/pVNm7cyLPPPsvb3/521q1bx9VXXz1jP9QXv/hFtm/fzsMPP0xTUxMf+9jHuPvuu3nggQfmPD6FQqGYCxs7k2zoSHBkuECu7NIUNriwNbYgSpHx3LK5k5s2dqiLVoVCcVoZzld4+kigBNnVk+b5OSbFbFyZYENHgs2rgvbCkKVh6TqW+uxaUjyyu69efHc9iaELVibDvPf6tar4rlAsMuNtKT7wgQ/wwQ9+cE7biEQi5HK5MY+Vy+V6emwkEqFcLk9Y3tzcPO12v/Od7/Cd73xn0mUPPvjgmN+llAghzt7CiJRy2t9ns85smKof6nWvex0//elPicfjuK7L6OgoQggSiQTAjP1QDz30EHfddRcdHR0AfPSjH2Xr1q1nVXyQQqE4e9E0ccqRvLPFMDQVyatQKBYN1/N5vi/Dju5RdnWneaYnPT81SFeSK7qSJMImIUPDMnSlBlnCPLK7j3se3UPJ9khETEIRjYrr0z1c5J5H9wCo4ohCsYg8/vjjxOMnryWnUotMx8UXX8z27dvHPHbgwAHWr18PwPr169m/f/+E5a95zWum3OYHPvCBOY9jqTKrwshPf/rTaX9fKKbqhwKIx+McOnSIN77xjXiex3/7b/+tnpc8XT9ULpejv79/TD9VW1sbyWSSffv2qcKIQqFQKBQKxSR4vuREpszT3SPsPJrmmd40Lx7PzUoNApAIG/za+jZetrqZjSsTjBRtnj4ywkihwqHBHPmKw3DB4dhghod29WP7YOnwid+6jLdsuRDD0HBdn5++OMA3fnmU4UKFVc1hMiWbE1mbQsVhuOjhAy0RnYfffzVHR2wGcxUiIcG2HcfoS5dIRUxesTbFriNZXM8jW7bpHSnjARtXNvGl33s5qaYwvi85MJhj+4FhSo7LJe1NoMFw3sb2PHpHyxzPlOlKhbnpZR1Yuk6h4hEL6fhScuBEAZBcvLyJNW1xNE1g2x7bdvfRly7RmYrw5s2dWNbkMe6+LzkyXKA/U+Rr248yWrRZ3RLjE2/cQDhksP3gEAPZMr6UxEIGuqbR1RJi29N97B3IEzclPekKmbJHR1OYL7xtM4/vH6EvXWJZwsL1fZ54aRgJbFrZxIXL4hiaTtTSKNo+mhCsWxZDE4JCxaMpbNCVjPDEwUE+/a97yZZdWmMmhh74ukggYmrkKi73P36QmzZ21F+zRuXiNRe10pspkSu7xELBsde2P15BWTsHC62wXKztKhSni3g8PqYwMh9uvPFGPvOZz/C1r32Nt73tbezYsYPvfe97fOELXwDgLW95C3fccQe/8Ru/wZYtW/jmN7/J8PAwN95445TbPO8KIzNh2zaGYaBppyZ5nKofqsaqVat45pln2Lt3L+9///tpaWnhPe95z7T9ULX+pmg0OmF5bZlCoVAoFArF+Yzt+pQclxeP5+pKkOf6MvSlZ68GkVJSqHhELJ14SMcydJ47nmOk5PCdp3voS5cpOx6uP7WquOLBR7a9yEe2vcg7rlnNtl195MonCzEvDuQnXW+45PFrfx3MhGoCxu/il4dHJ13vPw9n2Pypf2d1S4SLWmM8dXSUiush5cRtNPL5nx2iKWywrClEtuRQsD0EQXtQPGzw8gtaSEUNHn3mONmyiy8lmhB8+od7ederL+KO164fs73n+zI8vLOXbTt7SZfc+uNPHU3z8K4+IqaGrmuUbRfPh5rf7FRjPJ6pcPWnH59y/D/dOwiAINiWJgSmLhBCELN0liXCuL7PYLZCznZxXFnfrq4JfCmRANX97+3P8dkf7+OylYkx7TYIsDRBeyKEoWmMFoPozuaIRUvcYt2yOLdd1cXGzmT9HBw4kafiBJ5cjcvny2JtV6E422hubuarX/0qn/rUp7jvvvtoaWnh7rvv5uqrrwaClJqPf/zj/Pmf/zkDAwOsW7eOL3/5y6RSqSm3+c///M/zGstZ20ozGel0mvvuu48f//jHjIyM8OCDD2KaJl/5ylf4n//zf3LRRRct5DgBME0TgCuuuIJ3vOMdfO973+M973nPtP1QtYJJqVSasLzWT6VQKBQKhUJxPuD5EtfzsT2fE9kKu3qCIsjzfRle7M9SnmVSzLr2eENkboKv/8dh9vYXSEWDIkvZ8UgXHcquz+Gh+U1EfeOX3TM/aRKmK2hMRfdIie6REoYm0IXAnsVGcmWXQtmtKw8MXRA3DUq2x49f6KfiBucyZGoYQsOVknTJ4XM/CaTqteLI830Z7vv3/fzHwSHylcnVOCXHB8efsSAyVyRUi0ASz5cIAU719Su7Pq4nkeOeXyts1QpBvpT4Ev52+2EsU0f6kkTEBBNGCjZFzydXdoiHDBACAYxi0xoP8Vxvhr7REjdt6uD7zx5npGAHKW7JIMWttvzOG9bPq4hRO7cLvV2F4mxh3759Y36/4oor+Na3vjXl82+55RZuueWWWW//Ix/5yJySwc56j5HxpNNpbr/9do4ePcqqVavqfiKZTIaf/exnPPvss3z7299esDaVr33ta+zevZt77723/pht2/U0nOn6oZLJJMuXL+fAgQP1dprBwUHS6fSEuCKFQqFQKBSKcwHflziej+P7OK6k5LrsO57n2d40LxzL8sKx7KzVIM1RMyiCdCS4vDPBpcsTNEVMwqZO2NRwbY//PDRK2fEpV9UWZyuWDmV39gfgA9KXxEI6ricpux6tUYvRogOAqYGpBUoMC4GhCYq2x4O/OMy7rwti1B/e2cuJTGHKokgjUoKhiWlVN/NFAroIfLFy1bGYGkxVK5MEBRUBaEDFk9i+y4UtUYQQDBcCdUjUDFp1shWXC1qCScls2WEgW2LzqhQHBwvc//hBIqbO+uVN9ZuseNhgXSjOgRN5vruzjw0diTm1v/i+5OGdvYwUbNYtiy/YdhUKxUnuuOOOCYWRYrHIV7/6VW655ZazyrZiXoWR//f//h99fX387d/+LZdccgnXXnstEKTVPPDAA9x555184Qtf4C//8i8XZJAvf/nL+exnP8sPfvADfv3Xf51du3bxjW98g49//OPAzP1Qt956K1/84he54ooraG5u5i/+4i945StfyerVqxdkfAqFQrFUUH3UCsX5RWMBxHYlbrUQMpQr88LxbL0IsncOapC17TE2VE1SL1+ZYGUqQsjUCRk6IVMjPM4o9Y+2PV8vBJztlN1A/SCA2ZYeavoJQxfYbqAKqeFVW3L06unShMAyNLIll227+3jFRS0cOJFnKO9OtulJ8Rex8iTl2ACF6c6FaFiu19qXJJRdH0PTsF0fQxNIBAgfKaHkeEQtg6hlkCm55KteIwcG81y5qnnCDZYQgo5khP0nchwZLszJyPzIcIEDJ/J0JCMLul2FQnGSyZJxRkdH+epXv8qb3vQmrrnmmjMwqvkxr8LIT3/6U377t3+ba665htHRsT2br3nNa7j99tv5t3/7twUZIMDGjRu57777uPfee7n77rvp7Ozkox/9KL/5m78JzNwPdccdd+C6Lm9729soFAq86lWvGqM+USgUinMB1UetUJy7SCmxPR/HC9obHK9aEPF8XM/n0GCBF45n2XMsy57jWXpnmRTTqAbZsDLBJcubiFg6ph6oQcKWPqEQMp7u0eJCHeYZZ75CDCklmiaQUuLMsBFdCGzp05cucWnZpVJV2sx+X/Mb46y2DRNaZ3QRFHjG77b2uxAQtgwKFRdJ0K6lCRlI5qutNrVxe9Vzo2sCr1rU04XA9ST6FFaFEUtnIOuTK8++eARBq1PF8YkkJze7ne92FQrFucm8CiMnTpzg0ksvnXL52rVr+Yd/+Id5Dwom9kO97nWv43Wve92Uz5+uH8o0Te666y7uuuuuUxqTQqFQNDLeef+6tW0YxqmZUM8X1UetUJwb1Aogri9xXR/Hl9huUACRVcPLdNHhxaoaZM/xLHtnmRQzXg2yYWWCjkQYTRPomha0xhgalqkRMia/mZyM1c1Rnjoyubnp2cZkxq2zQQiBlMF/zRlUel7ViLUzFaEpbNRVODA71Y0Qi1ccEdV/jb9LgvYagcSdRHSUilhYhkah4tZ9RzQhTp6T6haFoF5g83yJrglMXaPouRi6wJtC0FSyPUKmRlN4brcttXNbsj3ik6w73+0qFIpzk3l9ErS2ttLX1zfl8pdeeonm5uZ5D0qhUCiWOo/s7hvjvG/ogpXJMO+9fi23bO48rWNRfdQKxdmHW1N++D6OFxhf2q6P658sgCADo8sjQ6egBqkqQWpqkLCp11NIQqZOxAgKIpahzclAr5FPvHEDD++a+rrwbCJsiHo7zWwJzprE9SQhUyMVMRmpthbpIii21PBl8DqnoiZv3tyJYQTKvlyxTG9mdvvThFi0dhohqL4PZHVf4PpgCAibet17BMDQIGLqpKImvl99XEC4+l6yDI2K42FogdxEVJ8vpaRou7TGLOIhnYFsmZXJMNmSzfJEaMz7UErJ8UyJTV0pLmydW2jCha0x1i2L81xvhnWh+IJtV6FQnJvMqzDymte8hm9961u89a1vnZDssnPnTr7zne/wxje+cUEGqFAozg/OBm+M2hgfffYYX9t+hIrrk4yYhCIaFdene7jIPY/uAagXR07Hcak+6rOHiuuhC4ExlWZccc5RL4B4QQEkKIT4eJ5EIgNPh4bnZ0rzU4NoAtYti9fVIJd1JOhIhhHVFJDajWrY0AlbwX8X6rMoHrPYur6VX+wfXpDtnUlsLzA3tb3ZFR40AjVF2fExdEHY0BktOYSN4HvB9cHxZbUdRdZ9N9619SIsK1Dl3HZVF32jJeJDpRkNWIU42Y6y0NQ9Q3xJU0ivp9KIqozE8WVdQSIAQ9OIh03Kjke25BAL6QghGMxVSERMopZOxfEoOj66BvGQwWjRRhC0sSxPRDg4WKAlZvFfr7mA7z97vP5dFrEC1ePxTImWmMWtV3XO+f2qaaJ+bhdyuwqF4txkXoWRD3zgAzz22GO8+c1vZsuWLQgh+Na3vsXXv/51nnjiCeLxOO9///sXeqwKheIc5WzwxqiNcX9/jh3do1Rcn7gV3FhomkbE0gibGieyFR54/BA3bexg70DutByX6qNeukgpKTseJdun5Hi4vs+yppAqjJwnDOYqFCvupAUQCG5wjwwV2HM8KIK8cOzU1SA1NEHw2VRNjgkZgSpksfj7d13N2x/8z0mLIxcvjzNasBnM23Pa5juuWc22XX3kyrP334D5tcOsbolwUWuMp46OUnG9WW2jKWywrClEtuRQsD0EgbloPGzwmvXtJKMGjz5znGzZxZY+mhCkoibv2npRPaoXYGNnkjtvWE9nc4RtO3tJlyZ+VkdMDV3XKNsunjypQjnVGklQNAtUKKYetMBELZ1liTCu7zOYq1ByfNxqkSRi6SQjJhXXw/YkuXLQBnNBa4z3XL8GYKya0tCIWjrtiRCGpjFaDN4DqYiFRLKpK8WtV3WysTPJ2vZ4/TtzIBt8ZzYunw+1c7vQ21UoFOcesyqM2LaNZVn135cvX84//uM/8slPfpKf//znSCn50Y9+BMCWLVv4+Mc/TldX1+KMWKFQnFOcDd4YjWMMev2D1hnbk4wUbFpiFiFDRwiNRMSkL1PiH351lF8cGD4tx6X6qJcWjudTdjzKTlAM8X2/fvMyz04FxVmKROI1tDycihpkbXs8MEldmWBDgxqkhhCgIbCqfhURM0iQmW97zHz4+3ddTb5g8/FH99A9WmR1c5RPvHED8ZiF6/r8bP8JfvjccUaLDuuWxehqiZIuugwMZ3loVz+2H8TlfuK3LuMtWy7EMDT+7KbL+emLA3zjl0cZLlRY1RwmU7I5kbUpVByGix4+0BLRefj9V3N0xGYwVyESEmzbcYy+dIlUxOQVa1PsOpLF9TyyZZvekTIesHFlE1/6vZeTagrj+5IDgzm2Hxim5Lhc0t4EGgznbWzPo3e0zPFMma5UmJte1oGl6xQqHrGQji8lB04UAMnFy5tY0xZH0wR3/8YGtu3uoy9dojMV4c2bO+tKkUY2dibZ0JHgv159Af2ZIl/bfpTRos3qlhifeOMGwiGD7QeHGMiW8aUkFjLQNY2ulhDbnu5j70CeuCnpSVfIlD06msJ84W2beXz/CH3pEssSFq7v88RLw0hg08omLlwWx9B0olYQp6sJwbplMTQhKFTTYrqSEX55eJiBbBkpYf2yOKmYVX98Mo+tmzZ2jPHfuuaiVnozJXJll1goOPba9htVlLVzsNAqy8XarkKhgH/+53+e8FihUABg+/btDAwMTLrem970pkUc1fwQUs7cpLh582Ze/vKXc91113Hddddx8cUX15flcjmOHDmC7/t0dXXR2tq6qAM+E+TzebZs2cKOHTuIx5UMXaFYKHxf8snv7wn6f5dN7P89cCLPpq4Ud9902Rm7gKmN8clDwziez4lshUzZRauayAkEEUujLR4CBJ7vM5S3uXJVkpLjn5bjOhvO47mMlJJKtQhScjwc18eveUSMQwhYkQgTsU69SKW+mxaehTynni/5z0NDPH1kdF5qkMs6EvW2mItXNBExJ95MB59DGhGrmiBj6EqNpFAoFOcQS/27/tJLL520AC+lnPbxF1988XQMb07M6spsxYoV/OIXv+AXv/gFQgja2trYunUr1113Hddeey1XXHHFYo9TsQhM531wNvg9KM5+TtUb43SkwhwZLrC7O81I3saVkpChoYlAfhz0eUtKNhRtj6ilY7s+moDRosNFbfHT4vmh+qhPP64XxGuWHZ+S7eE1qEIUisdfGuQPv72b4cLMrSOzUYPUWAqqEIVCoVAoatxxxx3nzHfQrAojP/zhDxkZGeGpp57iqaee4umnn+aRRx5h27ZtaJrGJZdcwtatW7n22mvZsmXLmLYbxdJkOk8HYMn7PSjODU7FG+N0pcJkSg7HMiVc3ycVtZBSki45OFVjPgnYns9IwaZQFji+ZEUyQsTUiUwil57puOaL6qNefCpVRUjJ8bAdH7/qG6FQjOervzg8ZVEkFTEDX5AZ1CBw0v/B0LV6EUSpQhQKhUKxVPjgBz94poewYMxay9vS0sIb3vAG3vCGNwBBC02tULJjxw7+9m//li9/+ctEIhFe/vKXs3XrVn7/939/0QaumD/TeTq8eDxbjwdcqn4PitPHfBQZtu3Nqp8apvfGkFIymKtgex7poo3vy7ri4ZHdfdzz6B6KFZdYyCBqarie5OhQYUIqzKmSLTlUXJ+oqQMCIQTxkFGPYmwcb94O0gZed0k7L53In3bPD9VHvbB4vgxUIXZQDHE9f1IDTYViPL+xcQWPvzSILgQXtce4vMEkdeUUapAak0XphqYonCgUCoVCoVgY5n1V3tTUxOte9zpe97rXAVAqlfjXf/1Xvv3tb/PEE0/wi1/8QhVGliC+L3l4Zy8jBXuMF0E8bLDOivHTfYMAvPaSdjRNO7ksFOfAiTzf3dnHho6EutE6D5iPIuPzj+3nwScOky27+FKiCcGnf7iXd716rAN/jQtbY6xbFg+8MUIn348jBZvDg3n6s2UilsFXnjjMz14a5Larurh0eRP3P36QfNnFNDRyFa/er2jqGvmyW0+FWYi2mkTEJKRr2J5PWAaxhZ6UGJrA80/6SPgSEmGT5piJL2Fde5zn+sYeFwQFlOOZEpu6UlzYGpt8p6eApgkVyXsKVFyvqgwJDFR9qVQhirnzO69czU2bOhgp2DNGq9ZUIaZe9QmxdMKGjq6+ZxUKhUKhOG2c0nTl4cOH+Y//+A+efPJJnnrqKdLpNFJKurq62Lp160KNUbGATOfpkLeDmwCAfMUjETl5U7kYvgiKpUtNkVGyvaAwENGouD7dw8UpFRmff2w/n/vJflxfYhkahtBwq20nn/vJfoAJxZHJvDHKrsezvRkKZYd4yGBTZ5KQqddVS1vXtdI9WgpaWNxAoSE0gZTB7wBHR4tsPzjE9ZcsO+VzkYyYrExFOJYpky07WLpGxfGxDA3P85GAqWtcvjLBmrYYBdvnwGCet79qNX1p5fmx1PHrqpDAPNXxPKUKUSwITWGTkuORn6RlrhalGximaoRMnZAxd1XIbPzAZnrOVMsXw2tsoX2h5jvG+RzzTPtqVEuuSIZYlgiRLri0NVmsTEUo2f6MY6xto3e0SMjQedXaFlpjoUVV/o1P4tnQkWDr2vYZX5dy2eUr2w/TM1pkVXOU/37dRYTHqSBnc56nS6lpZC5q1FNl/LhXN0fpHi0uOSWm8gNUKBaWORVGisUiv/zlL+uKkL6+PqSUxGIxXvWqV3Hdddfx6le/mtWrVy/WeBWnyHSeDo7rB3cDIoibHM9i+CIolh6u63P/4wcp2R7LEiGECC6OIlZwEX8iW5mgyLBtjwefOIzrS6KWjlYtulkIDE1QtD0e/MVh3n3dmgkXMo3eGPsHcuwfyFN0PFamIqxpj9McCzyLaqqlH78wQNl20TUNSxf1Ap8QYAqwPUnF9hjIlhfkfFzYGmPz6hSVQ8O4nmS4YOP6ElNA2NIRwPJEhLXtcRCCiCUYyPqsSEaU58cSxXb9QBVSNU/1fH/RVSFF2+W5vgwvDeR5xYXN/OYVKxd3h4olRaAKEVjV1phagsyp3MRM5xVW+3yZ6TlTLd+8KsXunvSCeo0ttC/UbI5/LutNd8wwvfdao1qyUUloaEEBzNAEK5JhOlORKcdY20am5OBXi7P6T6CrOcLrLlu+KD5vz/dl+MwP9/LU0VEqrgcSdF2jMxXm/3v9xVO+Ln/2yHN8+6keKu7JD86/eWw/t79iFf/7litmfZ5H8jajpcCLpzlq0RKzJj0/c1Wjnuo5aRy34/lUXI+QoWPq2pLx3Zvv+1+hUEzNrAojX/nKV/j5z3/Orl27cBwHTdPYsGEDN910E69+9avZvHkzhrGwvfKKxWE6TwfT0IKrN4IZ8PEsli+CYmmx/eAQxzJlEhGzXhSpIYRGImLSlymNUWRs291HtuxiGVq9KFJDq94MZEsu23b3cfsrJxZOa94YT+wf5P/+ZD/NUZMViXBQ7ajvO1At7e3PBPU7waSJL0IEF6ULdaPbqGoZzldojlnsP5HDEAJPSiKmwUVtsfpYG/9O1rTHlefHEkBKSbmhPcZ2feQUcboLRcXxeOFYll09aXZ1p9nbn62n1vzt9iN8/84Yl69UF6/nOpoQNIVNwqZGyNCxFig1azqvsJofGDDtc27a1MH3nz0+YfmTh4Z59JljtDeFWNvetCBeY/NRIZ7q8U82xqnWm+6YZ/JeW5EM8a1f9eD6El2IMZ8rrg+a7+NpgmOjJVJhc9Ix1hSXjn9SrqYJ8CR0j5b40Qv9C+7z9nxfhv/18LO82J8DwNI1BOD4ku7hEn/+Ly8AE1+XP3vkOf7ul93I6hgFwZArruTvftkNwG+/fPWM57ktHma0ZFO2veA7Xdq0xqwJ52c+atRTOSeN464YHs/2pslXXGJhk01dScKGfsZ99+b7/lcoFNMzqzvcz372s1iWxW/+5m9y/fXXc+2115JKpRZ5aIrFYCpPB4B4w0x/PDR2Vn+xfREUS4fBXAXXk4QiGo7n101PTV0A1SJH2WUwV6mv05cu4UuJITSklGNiSzUBuhDY0qcvXQKmln+mohaWrtHeNLYoUiNiBWkMpqbhuD5m9UKuhiRQPoVMnfXLFq7da7yqxdQ0io5HRyI8RtUy2d+J8vw4M9TidEvVFhl/keN0Hc/nxeNZdnWn2d2TZs/xbD25SHH+0hYPLfg2p/UKqyrrHt7RCzDtc+5//CARU2f98qb68lhIx/UkJdvD9WRwLSDEKXmNzUeFeKrHP9kYp1pv2mOewXvtpf4sj790IlBLmhpFJ1Db1ooFAD4Q06HiSQ4NF3jDZcs4OFSsj9F1/briUgekoD5u4Us8CUP5CoPZ0oL5vPm+5KGnuzkwmAcgbJ6c1DB0KFdbwO5//OCY16Vcdvn2Uz2BmqVhnI1j/fZTPUjfn/Y8O65Pf6aE7QZpb0IECXAD2QpXrkpyYLDAd3f2sa41Nm816nzOyZj3B7BvIIsrJcsSIbJll+7hAleubmbdsjPnuzff979CoZiZWRVGDMPAtm3+7d/+jdHRUYaHh9m6dSsXXXTRYo9PscBM5unQ6H2wpj0GEg4OFpQvwnlKe1MIAQzkKoHXQtXY1DKCmU/fDyTQ7U0nL/g7UxE0IbCrLQl+zaShmq5QS1noTEWmlX9Op2iCQI0RtQw6UmH6RkuUbK+uUvGlxHZ9dE2wqiVCKrawseGNiS/P9KT5p529VJygOOP5Uv2dnGFk9fUfE6e7iKoQz5e8NJBjV3eaXT1pnu/LUHEntiA2Eg8ZvGxVkt975WqlFlHMm+m8wmrKumf7MgiY8jlNYYMDg3muXNU8Znmu7JItuyQiJtmyS67i0hQ2x2x7rl5j81EhnurxTzbGqdab7phn8l5zpaTiSixD4CPwJUz2ye/J4Du0WPEYyNtjxvjU4RGyZRdDF3j+2PU1TYAvcVyJh1gwn7cjwwWePpquqzDGKz0tQ8d2PXpHx74uX9l+mIorqx4549ShmkD6wfl4fP8wL+ua+r01WnRAQNQy6s+JWgaZkkPe9urn5yvbD5+SGnWu56Tx/ZErO2RLbnWMWnV8LrnqMZwp3735vv8VCsXMzKow8uSTT47xFvn5z38e/PF1dNR9Ra655hqampoWe7yKBaBx9nsy7wNA+SKcxySjJpKg9SBsaBiGhpRQcXxsN1CJrGmLc93atvo6b97cyad+8CLZshtcMAXiEiC4gQwSWwzWr4hPK//8wOvWTaloqqsxqu/Bx18aZLhQoeT4+L6PpgniIZ2WWIjr1rYvauLLmvY465c3qb+TM4znS0pO4JtUtD28RVSFeL7k4GCe3dXWmOf6MhRtb9p1IqbOFV1JNq9KcdXqFGvb4xi6CNrEFIp5Mp1XGFCf0EAEP0+GLgSuJxnfNet4Pp4viYR08pVgZn/8tufqNdaoQpyMyVSI0zGb459sjFOtN90xz+S95lQ9NnRBvYAyGYGiMijgl22PZU3h+hhrikuz2p4pJimqy+o4K87C+Lzlyi7FSmA0PVkNX1RbZFxfjnldekaLwfIptlt7vFjxJrz3Gs9z0fYQAoyGnRuaoORLHNcnGbUYyPr0jBbratTJGK9GPRXGvz8cNxhvbYx6NYmu9j44U757833/KxSKmZlVYSQWi/H617+e17/+9QAcOnSoXiT53ve+x0MPPYRhGGzcuJGtW7fy6le/mk2bNk2oZCqWDo2z35N5HyhfhPMT35c8sjvo/z2eLmN7EkvUZoeg7PiEDI13v+aiMZJnwwhUH7u608GMmQCBQCLrM2jrlsX5l93HppV//vOuY9x6ZeeUiqaWmMVtWwIjvL50iZaCiV5t3xFC4EmflljotCg2ZvobUiwOFccL/EKqBqqLFacrpeTIcJFd3aPs6knzbG9mxgtNy9DYuDLB5lUprlyd4pLlTRiT+DUpFKfCbJR1kaox9FTP8aSsKxQaMXUNXRNUquo7c1xry3y8xtqbQhh6sM2INfHvwXb9CSrE6ZjN8U82xqnWm+6YZ/JeM41goSfH3uSPJ1A1Bv8NV7/TamOsKS69qnmWlBM7SUV1/wvl89YUNoiGdEQgSJlQHKmJPg1t7OuyqjkaLJ9iu7XHoyF92vNsVM1JXF9W23SDn2vnv3Z+VjVH0arKHGuScoxXNWLtTEXmeyrqjH9/mEYw3toYvdr4qu+DM+W7N9/3v0KhmJl5/dWsWbOGNWvW8Pu///vYts2TTz5Zj+390pe+xOc//3kSiQRPPvnkQo9XsYBM532gfBHOT2oSzY0rU6xIVHjxeJZiVUqsCUE8ZNCRDLOpKzVhvVjI4OIVTRwZKgTmlkgEEDI0LmyLoQnBc31ZVs4g/4yFjFmluTQ+p+L4WKbG+mXJ06rYUH8ni4/vyyA9xg5aZFzPX5Q4XSklvaMldvWk2V31CUmXnGnXMXXBZR3VQsiqFJd1JBbMXFOhmIrpvMLGK+ue65v8Obmyy8pkmGzJZnkiVF/eFDZIhA2OpUusTEVoChkTtz1Hr7Hr1raxMhmme7hI2NTGtNNI6ZMtOVzQGhujQjzl459kjFOtN90xz+S9ZghByBA4nsTSgkmEydppdBG0mMTDBsvjFgeHivUxdiUjfPqHe0mXHDSqAhVxMiLYk2AZAh3J+mVNC6KGvLA1xssvSHFoMI/t+mjm2FYV2w3UJF3NkTGvy3+/7iL+5rH9VFyJqPqP1fCr6tCQIbh+fSsv9hemPM8dyTBCCEaKNomwiRBBcldrLETc0jkwWGBTV4r/ft1FfHX7YdIlB0MTY8ZYa59NRU3ePI9Uo8nOSeP7oylkkIgYjBRsEmGjOj6LprBxRn335vv+VygUM3PK5UTLsrj88stxHIdIJEIoFGLXrl1ks9mFGJ9CoTiNNEo0L2yLsbolQn+2ErTVmDrtcYvukdKUEuWXdaXY3JXk8FCRoh305l7UFkUieP5YBuTU0u5G+efLVqVmVGMoxca5Sy1Ot+h4VNzFi9Ptz5TripBdPWmG8/a0z9cEXLqiiStXN7N5VYrLVyYIm6dm+KdQzJWZvMLGK+umes5/veYCvv/s8QnLDV1Uja4F+WpLxKl4KBmGxnuvX8s9j+7hRLZCImJiGRq2GxRFIpbOe65fMyvj1dke/2RjnG696Y55Ou+1tqYwt3cl+daveig6Pnq1Xabx40oDyh6YmmBNa4yDQ8UxY7QsnXe9+qIxqTS+J8eoOdriIdoTkQVTQ2qa4K0vX83Oo2le7M9RdnxMXdRTaXwfEhGD916/dszrEg4b3P6KVfzdL7vxJEhf1o1mawWh21+xqp5KM9V5Ng2NtniYvO2SLtpIIGrqLE+EODBYqJ+fcNion5ti1VNMr7Yc2VXlybu2XnTKxqu1czL+/XFBS4xs0eFEtkI8bLK6NUahcmb9xOb7/lcoFDMzr8LIwYMH2blzZ/1fd3cQzxUKhdiyZQt33XUX11577YIOVKFQLD7jJZqaprGyQaKaL7uzkiivHZcIky+7RE0dydTS7vHyz9moMZRi49xgYpyutyiqkKF8pe4RsrsnzfFMedrn11rAaq0xm7qSRC0lT1acHhrTuyKWxrF0iaPDBR7ZeYyi49Ics7i4Lcy+oRLFiovveZRdn2zZ5v6f7ed3ru6iP1vipWNZnj46CoChwa+tSRDSJf/45FHShQoD6SK7jo4GSSO1nWswmLfZczxXH48FxCM673hwmJaQ5Hjeo+wEiSZ/9OsX8H9/3E2h4jHeieND17fwdA/oSIq2R77qzaMDIQNM3eSh/zzAPY88y2g5aNf84GvX8Adb12EYGoeG8uzuHuWhp3sp2i7r2mO8fuNyViRD9I7k+emLaRxPEgsZ/ObG5fzu1RdNqRqseax956mj/MehEYoVj2hI57o1rVx1YQu7e9Lz8l5bngjz4BOBUWhjIk0tucXUBCtSEWJhg/XLmiYoG2txsw8+cZhMycGXQaHBENDZHOGGy1YsuBpyY2eSv7xtE5/54V6eOjpKxfVAgq5rXNAa4UOvXz9phPL/vuUKIEifqbgnP6VDhuD2V6yqL59M+Xn1mjZetipZP8/NEYtRgoJ0KmohYYI6tPHcZMsutvTRhCAVNXnX1osWLKq3dk7Gq1E7UhEqrkfI0MkUHcqmd8b9xGbyClQ+ZwrF/BBSzjwPt2PHjnoRZNeuXWQyGaSU6LrO5ZdfzjXXXMO1117LlVdeiWUtbBLEUiCfz7NlyxZ27NhBPK5uwhTnLr4v+eT39wQSzWUTJZoHTuTZ1JXi7psumyChnWm9KzqTSCTP92XntG3FuYnjBUWQsrN4cbqjRZtnqmqQ3d1pekZnNuhb0xZj86oUm1eleNmqZD2dYqEQAlYkwkQWoMCivpsWnqVyThvTu/rSJfoz5SnNfkNG4F9xLsZDX7o8zqGhAvYcjm3zqiT/fMfWKZd//rH9QQGi7NT9PJJhk3e9+iLed/26KVWIU8XM17Btj227+9jVk6Z7pEC65FC2PSKWwcXL47xmfTsvW5WaVtlY20bvaJGQofOqtS20xkKLqob0fcmBwRzbDwxTclw2dCTYurZ9RgVPuezyle2H6Rktsqo5yn+/7iLC4yY9pjpnjY/Hqi1KhYo3rfKzdm760iU6UxHevLlzQZQikzF+3Kubo3SPFpecOnWm96Ti3GCpfC+dD8zqyuxtb3sbQgiklKxZs4abbrqJa665hquvvlq9QArFOcRiSJTHS7uPpSfKa5X889xHSknFORmn67gLH6ebKzs805MJCiE9aQ4PFWZcZ1VzhM2rU1y5qpnNq5KkoudecV9xdvF8X6ae3uX7kmOjJcrTREE3ztqfa+wdyM95nd09Gd70+V9MWhz5/GP7+dxP9tdjao2qsWe65PC5n+wHmFKBMJNC0bJ0Lu9M8u97T2C7krVtTfXvuJ6REv/6fD/rlzdN+x1nWfopx87OFU0TXLw8wcXLE3NaLxw2+MAN06s1pjpn81F7ns5zM9n4lqI6ValmFYqFZVaFkZtvvpnrrruOa665huXLly/2mBQKxRlkvhLN2a6n5J/nD67nU3arqpBFiNMt2i7P9mbqrTEHTuRnLLSsSIS5cnWqrgqZbRKGQnE68H3Jwzt7GSnYrG2L8qM9JyaNiVVMz+6eDNl8hUT85N+3bXs8+MRhXF8SbTBVtRAYmqBoezz4i8O8+7o181IiNL52UyWvfXdnHxs6EmoCQKFQKJYgsyqMfOYzn1nscSgUiiXEfI1NZ7OeMk09t6lUFSElx8N2fHwWLk637Hi8cCzLru5Rdvek2dufm7HQ0hq3uLKaGrN5dYqO5KnHOp4K6l2umI5aMlhHMsJA3qZoe4tiPHw+cNfDz/LA77+i/vu23X1kyy6WMTaBBYIYXcvQyJZctu3um5cyofG1my557chwQc3yKxQKxRJEucgpFIpJma9EU5mmnl94vgxUIYsQp2u7Pi/2Z+uKkBePZ2f0UUhFzEANsjoohnQ1T7xJOZ0IAQKBoQtChk7IEJi6ivRVTE5jMthgrrzg7WbnEz3psZ5CfekSvpQYYvK/P10IbOnTl57Zi2gyGl+7yWhMXlMoFArF0kMVRhQKhUIxJyquV1WGBAaqvlwYVYjnS/b159jVM8ru7jTPH8tSmcZbAYJEpE1dyUAVsrqZC1ujZ6wQIggKIZqmYRkalq5hGUFBRBVDFLOhMeErXG338FHFkfmwKjVWHdaZiqBVPUWsSbRbnpRoQtCZmp+qbHw623jGJ68pFAqFYmmhPp0VCoVCMS1+XRUSmKc63sLE6Xq+5OBgvq4IebY3Q8mZPHmjRsTU2dSVrEform2Po5+hNiwhQENg6Bphs1YICf6dSZWK4uzlwtYY65bFea43w9q2KFFLJ1f2Fz67+jzgs7dtGvP7mzd38ukf7iVdcjA0MaadxpcS2/VJRU3ePElE7WxofO3WhSYmrx3PlNjUFaTSKBQKhWLpoQojCoVCoZiA7fqBKqRqnroQcbq+lBwZKrC7J82u7jTP9GbIV6aXlYcMjY0rE/XkmIuXxzHOgPqipgbRq2qQkHFSFXImxqM4N2lM+Do4VGRNW4w9x7N4MyinFGPZvCo5xngVglSTd736Ij73k/0UbQ/L0NCFwKsWRQxN8K6tF807Ana+qW4KhUKhWBqowohCoVAoJsTp2q6PPEV/AyklPaOlk4WQnjTpkjPtOqYu2NCRqPuEXLYigWWc3sJDrQgiROAHEjI0QqZW/Xl+N00KxWwZn/C1sjlCf6ZM0Z5cTRUxNXwpz8nY3kuXxzk0VMCewVuokc2rkpNG9cLJKN4HnzhMtuxiSx9NCFJRk3dtvWjKqN7ZMt9UN4VCoVCceVRhRKGo4vtySSal2LbHtt199KVLdKYivHlz57xntBSKRhYjTvd4psTu7jS7eoJ/w3l72udrAi5dkahH6F6+MkHYPL3v78AgFYxq4cPSRVUVoi+JzwDF+cf49K6IpXEsXeLocIFHdh6j6Lisbonxf269gpGyS6bkcCJT5nvP9tKXqdCVDPM7V3fx+X8/zJGhHCdyDrqAZNTiM2+5nJ/tG+bF/hyO64Hvcmgk+DvtSpr0Z23yto+QkmJDLWZ9E2SlieNLWkKS43mPsgNhU+OPfv0C/u+PuylUPMZrWz50fQtP98Bgvkyh7GB7IDSNLavilF2f/pxLa0iyb6DIaNknZGh88LVr+IOt6zAMjUNDeXZ3j/LQ070UbZd17TFev3E5RwZLjJTK/OrgKAXbY21bnL9+y6YJSpHx3PHa9bz7ujWL9r2qktcUCoXi7ERIqYLgZiKfz7NlyxZ27NhBPK6SNM5Fnu/L1Gd4Kk4ww7NuWZzbruo6ozM8n39sf31my68awyXCBu969anPbCnOTybE6Z6iKmQwV2FXT7paDBllIFuZ9vkCWL88HihCVqXY1JUkap2+Gn2jQapZTYqxDIGl66ddmXKqqO+mhUedU4VCoVAsJdT30ulDKUYU5z3P92W479/3M1Kwg57gZNAT/Fxvhr7REnfesP6MFEc+/9h+PveT/bi+xDI0DKHhSkm65PC5n+wHUMURxYx4vqTkuPU2Gdc7NVXISMHmmZ7ALHVXT5re0ZmjLde0xeqFkJetStIUNuc/gDnSaJAaMjVCyiBVoVAoFAqFQjGOJVkYGRkZ4fbbb+eee+7hVa96FQA/+tGP+MIXvkBPTw+pVIpbb72V97///WhaMMO3bds2vvCFLzA4OMiaNWv42Mc+xpVXXgmA53l89rOf5ZFHHqFUKnH11VfziU98gmXLlp2xY1QsDXxf8vDOXkYKNuuWnXSRj4cN1oXiHDiR57s7+9jQkTitMljb9njwicO4viRajWwEsBAYmqBoezz4i8O8+7o1qq1GMYGK41F2PEpVA9VTidPNlhye6c1UfUJGOTJcnHGdVc0RrlzdXC2GJElFrfntfI40qkFCVWPUkKkMUhUKhUKhUCgU07PkCiM7duzgIx/5CN3d3fXHnn/+ef7kT/6Ee++9l+uvv57Dhw/z7ne/m2g0yh/8wR/w5JNP8slPfpIvf/nLbNq0iW9+85u8733v47HHHiMSifDFL36R7du38/DDD9PU1MTHPvYx7r77bh544IEzeKSKpcCR4ULdPX787LEQgo5khP0nchwZLrCm/fTJ17bt7iNbdrEMbUykIIAmAv+DbMll2+4+bn/l6tM2rvMd1/XZfnCIwVyF9qYQ161tw1gC7Re1ON2SHfiFnEqcbqHi8lxfph6he+BEfsbtdCTDXFk1S928KkXbDD3+C8FkBqlWQ1KMUoMoFAqFQqFQKGbLkiqMbNu2jfvuu48//uM/5sMf/nD98b6+Pn7nd36H1772tQCsXbuWG2+8kaeeeoo/+IM/4KGHHuKmm25iy5YtALzzne/k29/+Nj/4wQ+47bbbeOihh7jrrrvo6OgA4KMf/Shbt26lp6eHVatWnf4DVSwZcuWgxSCSnFx1EbF0BrI+ufL0kaILTV+6hC8lhpj8plsXAlv69KVnbmNQLAyP7O7j/scPcixTxvUkhi5YmQzz3uvXcsvmztM+nvFxup7vz0sVUnY8nu/L1Ftj9vXnZmy1aYtbdUXIlatSrEiG53cQc2Aqg1TL0NGVqaFCoVAoFAoFL7zwAn/xF3/Bvn37CIfD/Pqv/zp/8id/gmVZPPPMM9xzzz0cOHCA5uZm3ve+9/HWt771TA95ybCkCiNbt27l5ptvxjCMMYWRN7zhDbzhDW+o/14ul/nZz37GzTffDMCBAwe47bbbxmxr3bp17N27l1wuR39/PxdffHF9WVtbG8lkkn379qnCyHlOU9ggZGqUbI94eOKfQ8n2CJkaTZMsW0w6UxE0IXClxGLiTZ9XNWLtTEVO67jOVx7Z3cc9j+6hZHskIiahiEbF9ekeLnLPo3sAFr04IqUM2mMcn7LjYbvzU4XYrs+Lx7PsqibHvHg8iztDJSQVMevxuVetTtGZmqiwWkga1SCWoRHSdSzz7DRIVSgUCoVCoTgd+L7Pe9/7Xt7znvfwd3/3d5w4cYJ3vvOdNDc38/a3v533vOc93Hnnndx+++089dRT3HHHHVxyySVs2rTpTA99SbCkCiPt7e0zPiefz/OhD32IcDjMO9/5TgAKhQKRyNgbxHA4TLFYpFAoABCNRicsry1TnL9c2Bpj3bI4z/VmWBeKj7nZk1JyPFNiU1eKC1tjp3Vcb97cyad/uJd0ycHQxJh2Gl9KbNcnFTV58xlQKpxvuK7P/Y8fpGR7LEuEEFUVT8TSCJsaJ7IVHnj8EDdt7FjwthrHC4og5apxqj+POF3X89k3kKu3xjx/LIvtjg/UHEtT2OBlXUFbzJWrU1zYGl3cQogAgcCopsSEGlJiVMSlQqFQKBQKxcxkMhkGBwfxfZ9a8KymaUQiEX784x+TSqV429veBsA111zDzTffzDe/+U1VGKmypAojM3Ho0CHuvPNOWltb+cY3vlGPLIpEIpTL5THPLZfLNDc31wsmpVJpwvJY7PTe7CqWHpomuO2qLvpGS3WvkYgVpNIcz5RoiVncelXnab85syydd736Ij73k/0UbQ/L0NCFwKsWRQxN8K6tF513xqu+LzkyXCBXdmkKG6xujtI9Wqz/fmFrbFav1fjtjF+v0UukP1PmWLpMImIihMDxfHxfomkCUxckIiZ9mRK/ODjI6pbYKY1NSllPjynaLkeHC+QqHmFDo7M5MsFvZjI8X3JwMM/OaiHkud4MJcebdp2opbOpK1lvjVnTHl+09hSlBlEoFAqFQqGYG/l8fszvlmVhWWPN7Zubm3nnO9/Jpz/9af7P//k/eJ7HDTfcwDvf+U7+6q/+akwHBQQdFv/0T/+06GM/WzhrCiOPP/44f/iHf8hv//Zv80d/9EcYxsmhr1+/nv379495/oEDB3jNa15DMplk+fLlHDhwoP5mGBwcJJ1OT3hzKM5PNnYmufOG9Ty8s5cDJ/IMZH1CpsamrhS3XtV5RqJ64WQU74NPHCZbdrGljyYEqajJu7ZedN5F9T7fl6m/RhXHx/F8Kq5HyNAD801TY92yOLdd1TXtazZ+O+PXG+8l4vk+FdcHJAXbxXYlUsrqjb0gZhnYrs9XnjiMEGLOY6vF6ZYdn5Lt4fk+Lw3k+eEL/RwdLmK7PpahcUFrlDdsWMH65WNNgH0pOTxUYHdPmt3daZ7pzZCvTO+JEzI0NnYmA8PUVSkuWdG0eIWQcd4gSg2iUCgUCoVCMTeuv/76Mb9/4AMf4IMf/OCYx3zfJxwO87GPfYy3vOUtHD16lA984APcd99903ZYKALOisLI7t27ueOOO/jzP/9z3vKWt0xY/pa3vIU77riD3/iN32DLli1885vfZHh4mBtvvBGAW2+9lS9+8YtcccUVNDc38xd/8Re88pWvZPVqleahCNjYmWRDR2JaFcGZ4I7Xrufd161h2+4++tIlOlMR3ry5c0krRWZSY0zFdIkvz/dluO/f9zNSsOlIRijrLrt6RilUPKIhgytXp4iYBs/1ZugbLXHnDevZ2JmcMJZ8xeX//fRAfTuRZKAOqq33slVJvrb9yBgvkUzJoeT4pEsuZrXVQwiBlATqDruCLyUD2TKXrkhSNj2e7c1QKDvEQwabulKETJ0X+jIM5yu8/9fWcVFb7GScru3hczJOd/9Anm/88giZkkN7U5iwqVF2fF7qzzGQKfNfr76AsKVXPUJGeaYnQ6bkTHtuTV2woSPBldXUmEtXJBZFnaHUIAqFQqFQKBQLz+OPP17vlgAmqEUA/u3f/o0f/ehH/PCHPwQC8cAdd9zBpz71KW6++WZyudyY56sOirGcFYWRL33pS7iuy6c+9Sk+9alP1R/fsmULX/nKV7jmmmv4+Mc/zp//+Z8zMDDAunXr+PKXv0wqlQLgjjvuwHVd3va2t1EoFHjVq17Fvffee2YORrFk0TRxWiN5Z4tl6WdNJO9MaoypmC7x5eZNK3l4Zy8jBZt1y+KMFmyeOjpKwXbRhSBbdnj6yCivuqiFdcviHDiR57s7+5BS8t1dfSfHYmgMFyoAbOpK1T0z4mGDdaE4+wdyPPjEYSquz/IGL5FE2GC06CAB15NEzGDMQoChCQq2jyZgc1cSXdfZN5DF832WJUKUHI+Rgs2WC1KsTIXpHinw8I5efvdVqxFMNE71peRHe/rJlBxWt0YRdeNdiRDw/LEs/993dlN2pvcI0QRcuiIohFy5KsXlKxOEzIUvpilvEIVCoVAoFIrFJx6PjymMTMbx48exbXvMY4ZhYJomF198Mdu3bx+z7MCBA6xff34p0KdjyRZG9u3bV//5S1/60ozPv+WWW7jlllsmXWaaJnfddRd33XXXgo1PoVCMZbyqY7wao6biGM9MiS8nsuW6/8to0WF3b5pCxcU0NHQBmhQUKi67e9JsXt1MRzLCru5R9g1kqTh+fSyDuTI9oyWiVlDoaImdrLQLEdzcZ8suLTGzXhQB8GSwzPEkEii7Hpau4UvqJqYRS6foSITj4riSzlSEeMjA8yUV12cgV0HXBLYr2dmTZuv6Nla1RMefCvpGSxwdLtIUNukbLTFcsBnO2zN6hAhg/fJ4kByzKsWmriRRa2E/3mtqEE3T6soZy1BqEIVCoVAoFIqlwNatW/nrv/5rvvSlL/Hud7+bY8eO8cUvfpGbb76ZG2+8kc985jN87Wtf421vexs7duzge9/7Hl/4whfO9LCXDEu2MKJQKM4efF+OUXWMV2PUVBwbOhITTE5nSnz5x1/10BI1WZEI14sdmgBdC/QUugBPQMX1OTKU54rOJMfSgXHuy1adVIZYukZI1/D84HnN0eYxSSu+Hyg2jHFKB19KdCEQOtiexPclZT/we4mYOroGzTGLmBWoJFIRMyiylFyKjstIwSZm6SSjFpahYbs+BXtsoWOkYPNMT5rH9g3yXG8G25teEQKwpi3G5qoiZFNXkqawOa/XbiqEAA0ReIOYGla9GKItakKNQqFQKBQKhWLurFu3jvvvv597772Xr3zlKzQ1NfFbv/Vb3HHHHViWxVe/+lU+9alPcd9999HS0sLdd9/N1VdffaaHvWRQhZFzgPl6OigUC8WR4UJd1TH+plkIQUcywv4TOY4MF8a0K20/OMSxTC3xRRu3nkYiYjKUrxCxdIbyNtmSS9TSqbg+UgY371IGKoaopZMpufSMlqh4PiuS4TFjMQ0NQ9fQBGRKLrmySyJyspigaaAJgTsuD1cTAiEEmgBDSi7rSJCKWiQjJsmIzgvHckgZFE0qFY9jmRKaJjA0Ddf30YWoe6WUncBIVUrJz/cPsruaHHNkeGbjq1hIJxmxCBsaH75xPZevXDhT4EY1SOANomGZwX8NXalBFAqFQqFQKM4Grr32Wq699tpJl11xxRV861vfOs0jOntQhZGznPl6OigWjukKUwsVL3ummG3RLVd2qTg+keTkPhYRS2cg65Mrj01LGcxVcD1JKDL5zbdlaPhlaI6a9GdKeL5PNGxgGUH6i2louF7wvg+ZGvmyy0C2TMjQaY+HxmyrKWSQiBgM5yto1djdGlJKXE+SCBsUKh7xkF8v1Ji6IGxq+L6kKxXm2jWtSAS251OyHU7kKpQdj85UUIiJWDqZkkMsBGXHIxWxsHRBf7bEkaEiri/5wD/smuAvMp6opdMas2iNh2iNWYRMje7hIpesSHBZR2KGtaenpgYxDY2QERRDLF1TahCFQqFQKBQKxXmJKoycxczX00GxcExXmAIWJF72TDGXoltT2CBkapRsj3h44sdKyfYImRpN45a1N4UwdEHF9YlYE4sjtutj6IL/smEFP3qhn750CdPxiYdMbLdCyfYwdY14yKTiBLG6q5otJFByfOKNagchWNMWJ1t0KDoejieDqFzb43imRGs8xOsuW8bXth/hRLZCWzxEImKgCbC0EJ6UJCImfekyQkC+4jGYK9cLMN0jJdqbwqxMRciWHAazFTRNMOhVODJcmLEQ0h4PsXl1ipXJMM/3Zai4/phUmu7hIsmIyX/ZsBxtDsWLmhpE12otMVVViKEvWkSvQqFQKBQKhUJxNqEKI2cp8/V0UMyPyZQTe45npyxMvXgsCwKc6o19wXbpS5dxHI9IyGBtWwxDg6cOj7DnWJY/uO5CbtywYsbXaiYFx0K1Vc216HZha4x1y+I815thXSg+RnUgpeR4psSmrhQXto6NBLtubRsrk2G6h4uETW1MO42UPtmSwwWtMX7vlavZvDrFRx5+lp7REiFdI2oZeFX/j4rjUfF8VjdH+bPf2sAju4/xXG+GtVaMfMXD8XxMXSMZMWiJW7QAjudxZKhAyNTY1JXi1qs62diZZG17nG8/1c1Q3g7aY1yPppDBKy9qYaTocHS4iO0GLTGXrEjwXzYsx/F8vv10D3v7s2RKDsWah4gngcn9QpqjJptXparJMc2sTJ1s/dk/kOdHe/o5OlxkKD92X+uXT+1I3hiXa+qBGiRkatWfl27Es0KhUCgUCoVCcSZRhZGzlPl6OijmzqTKifY4w4XKpIWptVaMx/YNUnY8NAFF28P1Jb4MblzLrs3usjNm1v8Tj+7hl4eGecuWVVOqR2ZScCxUW9V8im6aJrjtqi76Rkv192XE0utqjJaYxa1XdU4o0hiGxnuvX1tNn6mQiJh1g9JsySFi6bzn+jUYRlC8+PRtm/irH+5lJG+zIhmmLRZiqFChP1OmJW7xkV+/lCu6UhiaxovHsjy2bxBfntRqaEKwpi3GR2+6jFjIIF92SUQMulJRbN9nJG9zRWeK9e1N7OoeZTBfoSVmcdXqZnRd4EtJ32iJbNllIFOiL1Pm/p8f5Plj2XpCzVQ0hY16aszmVSkubI1O2bayfnmctcvW0jdaomB7xCydzubIBKVIEJcbqEHCpo6li7oaRBVEFQqFQqFQKBSK2aEKI2cp8/V0WKosVQPZqZQTTx0d4Xi6zOUrkxNubvMVj6LjUSi7aJrA1AXSC27OJYGIwHclhk7d1LNoezx9ZJRj6fKkLVAzKThu2tTB9589viBtVfMtum3sTHLnDevrxZmBrD9BjTEZt2zuBOD+xw9yLFMmW3YxdMEFrTHec/2a+nKAK7pS/K/fuKy+j6MjRUKmxjVr2ybuo3HoMvhdACEzMBTtao5ScTxKrsexbBnf96n5rgoNrrqwub6650v29efY1ROYpT7bm6bsTF8IiVo6m7qSXLm6mStXpVjTHptTC4wmxJhI3/FqkLChY5kqLlehUCgUCoVCoThVVGHkLGW+ng5LkaVqIDudcmJlIsyRoSLHM6UxLRAAtuNSqrhIwNLFmGWC4B5dAoYW3OBWHI+K49GRDDNSsCeoMWZScOwfyHH/44eIWvqCtFWdStFtY2eSDR2JORe5btncyU0bO9h+cIjBXIX2phDXrW2rp7nMZR+18+V6ktdd0k7R8dAQREM6iZDB0dEi23b28buvXAXV12I8vpQcHiqwq5oa80xvmkLFm+SZJwkZGhs7k1xZbY+5eHnTKXl4jPcGCTV4gyyFoqFCoVAoFAqFQnGusPTvmhWTMl9Ph6XGYhnILoQCZTrlhGXqhE2N0aI9IfY1U3HrbTO6puFLWRMsjKHW4aHrGq4ncXw5qRpjJgVHImJxcGiUK1elFqSt6lSLbpom5tW+ZRga11+ybNJlk72eU+3jyHCB7uEi65c1kYwaJGWg+Cg7HkNFB8eDHd2jXLeuta7IkFLSM1JiV88ou3rSPNOTIVNyph2vqQsuX5kIfEJWNXNpRxPmPKNtlTeIQqFYaixVJadCoVAoFIuBKoycpczX02EpsVgGsgulQJlOOdEUMkhFTfozFWzXA04WRmr9GMGNLiBPKkUakVIiZZCMomtgaWJSNcZMCg5dA9eTGFO0acy1rWqpFd3m8npKKcmVHcKGhmkI0kUH2/WxXb9+/k09MGs9Mlzk2d4Mu6vtMcMFe9px6Jrg0hVNXLk68Ai5vCNByJxf0aLRG6RRDWKppBiFQrEEWKpKToVCoVAoFgtVGDmLma+nQw3X9WfVugCnPnM02fqz8bJ4aSDLE/sHSUWtWe13IRUo0yonhGBlMsJoweF4pkzYNOqFqdGSg64FHhGO66NpAiFOKkTqm9AEjifRBEQtA8vUJ1VjzKTg8HwwdIE7fgdV5tpWtZSKbtO9ngOZMh+8YT0XL2+i5ATFo6LtkS+7DBVsio5P1DpZuCjZHsOFCgPZCiOFCh//lxem3bcmYP2yk4WQKzqTRKy5F0Ia1SCWoRHSlTeIQnGuM5fvzJmeW/uuHsiWcTyfkuNRcX1aYxbrlzfRHLXqherG7axujtI9WuToUI57Ht1LuuywPB7iq+/cwr4TRQayZXwpsXTBjqMZhJCsXx5nb3+Ox/cNUbIDnyyJJBm2yBVtnulJc/3F7Vy2som9x7L0jpaIh0xuuKSdXb2ZaoqXxj/+qoeRokNbLMQ/vvuVLEvF8H3JoaE8Lw3kAMHFy+Nc2BKje7TIQK7EPz3VR6Zks7olxl2vv5ho1JxwDhqvVzRNVLeXByQXL29iTVt8TFvlQr0GCoVCoTj3UYWRs5z5ejo8sruvbnbpehJDF6xMhnnv9WvHmF3Cqc8cTbX+ps7ktEqIsuuxfyDP//3Jfixdm3G/C61AmUk5UbQ9tq5rozlmcnCwUC9MXbe2DdeX9GfKmJrA8WVwY1ytW8iTGyFk6gigLR4ibukcGCxMUGPMNI5syWZlMkKu7LI8IRdE4XGqRbeFYPzrqQmBaWgkIyadqTDdIwUe2X2M21/RBZwsPC1PhulsjrCnL0PY0hnO2wwX7JMRutOwpj3GldXUmJd1pSYtRM1EoAYRGLogZOiEjJNFEHWhrVCc+8zlO3Om59a+q3tGShRtF6+h/i0Ay9C4sDXKJSuaAMFo0abi+DieT8X1eGkgR2Ng1nDB4epPPw4EPleuP3lBfTwjhZOKw13d6QkKyK/+x5FJ18uUi7zyr35GMmxw/SXLeProCPmqejFkaMTDBqMFm3SpUdE4yNd+eYTfuGIFn/+9LZNerzRHTZojFv25cn178bDByy9o4T2vWQOwYK+BQqFQKM4PVGHkHGCung6P7O7jnkf3ULI9EhGTUESj4vp0Dxe559E9wMmkkFNVYEy3/ksDuWD2axIlxEjB5tneDEXHozlq0t4UnnG/Cx1hPBvlxLtfs2bSwtT3nj3GPY/uoWh7NIUNfAnZkoPrSXRNEDY1dCEQQMwyWJ4IcWCwMKkaY6ZxtMZD9VSahVR4zLfotlAcGS5wZKjA2vY4yYiJEIE6pux4VFwP14enjoxwzZoWVrVEyZQcnulNs7s7zZOHRzieKc+4D6OaGuT5koil87uvWMUNly2f9RgnqEEMHctQahCF4nxlLt+ZMz33ZauSfG37EfIVF8fzxxRFICiy267PwcEC3SMlQobGpq4kqYjJs71p+nOVacc626LIeOazVqbs8i/PHKM5apKMmNiuz0jBZjA/eQujL+H7z/bTN/oL+kZLY65XchWXo8NFjsgiUUunJWYBkK+4/PylQQ4P5YlZBm7Vt+tUXoP5+pwplApHoVCcfajCyHmG6/rc//hBSrbHskQIIYKbt4ilETY1TmQrPPD4IW7a2IGmiVNSYMxGwVFxPY6li6xf3lRfLqXk8GCeQtlhZSrCikQYhJhxv4sRYTxb5cT4Qsv4CFrXk4QMnXhY0B4PYWgao6XggjAVtZAwrRpjNuNY2x5fcIXHfI1UF4J8xSVq6kQsnXzFpex42O7JSF1fSoZyFb7+y6N0Dxc5OJif8YK99nZJRU3ChlH385D4jORtvv1UD7928TJ0ffKLN6UGUSgUUzEX1SIw7XNf6s/y4BMjVFwfXUBpmnRw15dI6RExBf3ZoCBcdmb/PXc6abI0TEMjW3aYTYlld0+GmKWxPBGuXq9I7KoERgK252PqAiE0WgyNdNHmwIk8TSGD1166DE0LrnHm8xrM1+dMoVQ4CoXi7EQVRs4zth8c4limTCJi1osiNYTQSERM+jIlfn7gBINZm+37h2iOWjTGqgQGly4RU+eZ3jQHBnMYmjZhVuDIcIEDA3mawgbDhQqZoosQEDF1lidCNIUNsmUHKWH/QI6VqSgRS2cwV6E/WyYeMoKb8nFxt01hg53do/x8/wm6mqMUKoEqIxbST3pxhHRyFRfH9TENjaaQUffaiIV0Dg3mJ/RhTzWrMZt42MZlXckIvzw8jOP63PWGiwEYyTu0N4W45qJWejMlcmWXWCgo4NTGP9NsykzjONMKj1NFyiA5pmQHPfT5ssNIyal7hbi+z2jBYbhQYThvk66mxhwdKU65zfa4FfSdt8cJGxrfeqqHiKkRMsd+9Ak04iGDgVyFnd2jvOKiFuUNolAo5sRcVIvAtM81DZ1s2SUZMaYt5tf8q3wJhqYxUjWRzs+idfBMcDxboSOlY7sSOakt+eTUrlccLyiMCEAXQeJYyfGJWhoQFKyzZRfHh3zFIxHRGrYxt9dgripTRYBS4SgUirMVVRg5zxjMVQL1QmTyGzvLCC6s7t72AtmyQ9H20DXBvhM5NnQkaAqbHB7Kky25uH7QBvPeb+wgFbWCaNGGWYGXBnK8dCJHyXYp2B6+HxQ2NE2ga4KQIfBl4G1i6ILe0SKmrmF7HhHLYFNnkuaqRBZgtGBzaChPpuiQq7jc9dCzWIZGc9SiJWaxtj1Gc9TiwImgRSdX9qqJL4KmsI6pa6xf1sTf/+dRDg4WxvRhhwx9wvgbv7inUk6MnxXJlGwGcxUcL4jobfRuqUXRnsoF1kwKjjOp8JgPjudTdjzKTlAM8f2TipBU1MLQBM/3pXF9SbrkTDCwHU9z1Azic6uGqZ2pkxe8P3q+H8+XkxY1dE2QjJpYdvCeaAqbSg2iUCjmxFxVi9M915cSXwalg9l2vEhOtsf40yhMziSuH0wm1FLZZoPf8Dzfl2POh5TgNfQYCXFy+4438STM5TWYj8r0fGex0gYVCoXidKAKI6eZ2fZcztVNvdHpfd2yGJoQkyoR2ptCGLqg4voYerCuVvVZkBKG8xXKrs9QvkIspGNUWwryZZenjowQrsaTSl9iez6uJ+nPlik6Ppu6klia4Bf7B3l83yBIyWihguOdnBOS1fG6vsR2IRE26GyOkilWKLsel65oojMV4T8ODuH4PsO5MrYfKAkODxZwPA+JwPZ8KPuUhKBYdhHAs2UHx5McS5eoOB5h08DQBZ7nc2zUxjKDKNRQWqcjGaFieDzbmyZfcYmFTTZ1JQkb+rz9UwbdMkeGiziej6lrtFaLOpN5t5zLTPfe9Tyfw0MFRosOmqDeGy4JiiT7+nPs6kmzqzvNC8cyOOOb6scRs3SuuqA5MExdneKCluiEmb8aLTELXQ+SguIRg5ARROSGTQ1dE9iuj6m7rGmP094UWtBzolAozn1mShAbnxA23XM1IdCEQHKyBXAmBIFvEoCmLc3iiKFRTWoL/s1Y7SY4F/WfNYEmoKaHEYIxrY9Snty+qU8sgs/lNZhropti4b3eFAqF4nSiPu1PI7PtuZyro/0DPz9Ud3r3/ECpELN0ljWFaYlbY9a9bm0bzVGT3pESwTVDcAGha8ENbdEJrqQ8X5ItuUgZ3LTWLl0cb+LMScnxkdLm+d4MJcelaHv1G9rpLnkkUHI9SrbNoaEiuYrD/hN5QoYeFEBkcEHkS/CkBBkUcdyqGsOtbcXxyR7Pkggb2J6sr1uutloIQbUVQ3IiW+G1l7QjhGDfQBZXSpYlQmTLLt3DBa5c3cy6ZXP3T0FKnujP4fky2JcnKdgubfHQBO+WqSKRzwUa37u+D4mIwfrlcW64dBkVx+dHL/Sz/0TQxiQEJCMmzVGLntEiz/VlKDvTX8lrAprCJhe0RPmtl63k1y5tH3PRPBVCwCsubOGKlQlGi3ZQkBEajutTcX0qjkvvSIlVLTFeeWHLQp0OhUJxHjEmQcyKkau4ZEouIEmEDU7kKlzQGiNTcmgKG6xrj/Nc3+RpY47rkQgblB0fSxfYUxSJa3UFTUDF9YiFTCQQMQQ5e37mqotJMqzj+X6QiDND4buGLyUVx8X1QRKk0theoKQx9Vp7r4MuBLbnBabaGsQsjWzJqU9WxC2Nw8N5LmqNcWQ4T74SPPfwUJ6NKxMI7eR382SJbuMjg191QQtPHh2p//6KVc187/nj9KVLdKYivHlzJ5aln1cmpIvh9aZQKBSnC1UYOU3Mtudyro7293x/D3uP5+reHZmqasKpmpO1xkNj1gWIW0ZVclu7qPApVOSYIoaseYrMbkKHsutzPFtGr6pP8OSsOodtV7KjO4OUYOoavi+xXa/eiqIhMQxRl8p601wcZstuXWJr6oKwKZBS4slALisJepJrvdfZkkvUMhBCI2oZZEouubJLImLOOKsxflbkeLZM0faCtgsRmHParsTxJKZ+0rtl+8GhekvN6Waqi7PpLtrmss6e41ke+PlBirbH8qYQGoKK57Hz6Ci/OjRCyXbJ2x4CQbbsMFKwZ0xFCBsaV3Ql2dSZImJqREydtqYQm7tS9OfK7D46yj882U267NCRCPM/33AJTVELIUDTNExNMFKoYHs+qYjF26+5gP/zw33sOZYlYhlYhobt+mRLDhFL5z3Xr8EwtDHHFzV1etNFhvM2zTETDcFwwaa9KcR1a9vqha6FuPidq1LsfLnYVijOBmoJYi8ez/LjFwco2h5uvZ1DYBmCXNnlL3+wl5Cp0Rw1MXQxaZpYW1OYGzYsr6fSGBpjYncbqQkmsmWPfCX4fptll8ppZ6joIYoeusaElJ3JCBmCsiPpTZ9MGWt0JnE9GMhW6r9rAjpTEVIRk5+9NFRvw3F9ScXx0IWge7jIj/YMBJMt1YLUsUyJjSuTrEhGJk10Gx8Z7PsST/roWvCd73j+mNYdTQg+/cO9vPFlHeiadt6YkM5VNaVQKBRLCfXJdBqYbc/lpcub5uZov6OXQ4MFdA1SEYuhQgUkRE0N25PkKi79mSJXrm7m4GCBh3f0AhAydV5xQTMv9ucoVNz6zWntYkMTgeeClJJJWnSnxdJqEuDZXfRAYKYWD+kIBEXPRa8qWILZoaBtZypqY5YwRp5iCOoyWgmUbQ+vau5pOx5CBBGtNdmxrgW/1y5sZprVGD8rUrY9/IaLrEAhHFw8oQfeLdmyy+AM8YmLxVQqpM2rUuzuSU960QbMuI7vSxIRk3XLYuRKLrmSQ77icniwQK7sYHsSgSRX8epqptmia/BfNizjpk2d/GhPP7t7i9iuj+v52J5P72gRX0LI0AmbGrZX4kMPPcv65XH+5neu5NBgnn959jgv9efIl12s6vjf+vIuvv/scY5lymTLLoYuuKA1xnuuX8MtmzvHnKtj6RL9mTKO7we97NX3oqVrhC297h/TmAg034vfuSrFlOO/QrE0KZRd8tVCvaZpIIMiedmWDOVtVrfGCBs6x9IlDE2wMhVhtGhPmibW1Rzl/scP0jNSomi7Y75XBcEkgK4FN+aGdrL9xvN9hD+/aN3pqJVe57NdXVS/r2UQva4JMA2B48lJrxdMLZjwcV33ZOvMuH3X53BEfS6H0aJNyDyp/nA9n6Lt4UtwqlcMQojgWAToQpAvuzzTk2a4ECgKG1+DR3b3cc+je+qRwY7mM1yw8SUIPMKmRqWhahXSBUITjJYc/u6X3XSkwmxZ3XJemJCOUU1NooQar8JRKBSKpYQqjJwGZttzuf3g0Jwc7Z/ry+BLSSxk4vgS25UYuoYQwQWF60tGCg75ikdHMsKzfRkE0JGMEA8brG6NcmiowL7+HCFTw/N8RoqNhQDB3G5lwZUQmkVrw5hjIyimBJGDwaybJ0EXwd4jloFb9RFpbOupXQwhJ16kyXFJNqah4doerg+2HxRidC3Yp6mLuklrrZgy06zG+FmRsKXX2360qspGCFGfwbddH0MXZ8S7YioV0pOHhnn0mWO0N4VY29405qLtxWNZEOB6csw6Tx0e5qcvDtDVHGFtexOWqVEse/zq8AiHh/LYDti+jy/lmGjd+eD58C/P9vOrI6O0xUMsawrjS8nR4QJFx2N5IlKP27XdwMQ1XXToHS1y8//7BRe2xurH3BoP1Y+tJWbxl2++gnTJqUuga8qPxnPlS0nvSLFeOGy8cK+4PhFLp3u4yMcfeZ5VLVFChj5vB/65KsWU479CsfTwfck/7ehhIFehKWwQsQyklGRKDoJAtViwJ7ZttsYs7rxh3aS+YLds7uSmjR1sPzjEQLaM4wVG1RXXpyVi8YsDg/zy8AgRqZGKWjhe0HoihGQgU6bWnagDPkFxojUMRT8o6m/sTHLrlSv58hOHGSnYrEqFsH0oVjxs38VxYLjoYGjwmxvaGalA2fEwhM+eYzlGyx4aoFFrb50aTcDyRAjbg6Lt0ho1yVdcEIIrOuIcGSlzIlfBk5JlUYMTxUDFaRgaIREUl3RdIxHSGcwHJY6oIYhHTAJFjoYhJH3pMn2jJW7e1EHB9niuL4sQDq7nB6pFCbGQBiLwnNJ1CJuBkvai1hj/343rWdMWD1p3XZ/7Hz9IyfZYlgi+v4fyFaSspuLIoJ24EceXxEwd1/VxgaFchagZXA+c6yakNdVU32hpUiVUowpHoVAolhqqMHIamG3P5WCuMqfezGLV1dSoGkdKKesFlVpdwK2qIFJRi5LtgQi2A8FMVnPUImToNIUMyo4HBL4i851mCsbAxGmdWeB6frUFgvpdaHDxIaY1kptsN+NlxI0+FKYmaAoZJCIGIwWbRNgILtJiFk1hY1azGuNnRVY0hYhaOvmKi2ZqQfKPqVVNbYNWjQtaY1y3tm1uJ+UUmUqtFAsFPigl28OtKnYQwUXbWivGY/sGEcBrL2nHMHQsXaM5YpAtVfCq5rll16M/W2IgW+FYusRg3pnVmCxdw/H8Gd8epiYImTqOJ3lZV5KIZbD/RI64pVF0PLJlh3L1BmH86/3SQJ6oqXNpR2JS5dUjzxzn7psuG3Nx1niu1rbF+NGLA3gy+HspVOXpgkDJ4vlQsD1WJkL0pYMLwN/atBKtWliby8XvXFz8AeX4r1AsUY4MF3iuL1ufsDCrn3WeD6YRfO86XqA2aGzbPDCYRxOCl61KTbpdw9AmbcE8NJjn4V19aEIQDZkIoWFVr+oczwchEATF/xXJyBgz0pDnU7RdHE+yLBFheTLCJSuSE9ofsiWHJw8PB9sUBitTZv1xXS9iGYH/l4T693ZtcmD8Z7zngycFTWGDsKlTsF18BLoQxKNhNkbDPH1ktJ4c5ku37jVm6BqaRlV5KOrKFR9B2DTqx2a7PkLTAk+xnE08bGC7PhFTZ8QOrpmECPxJdAGGrtVVq7YXtARrDZMa2w8OcSxTJhEJzm/RDlS2mgiKAH5D23DtuH0JjusjhUAjKOgcHimytr0JOPdNSDd2JrnzhvV1VeNkSiiFQqFYiqjCyGlgtj2X7U2hOfVmRk09mNWvJssIEXg/gMCTMpgd0oKWkpLtEbF0RHU7te2bulZXTkBQZPGlrG5z7seqVQ0tTV1DytkrBmzXR9cEQlarKlW3eiGCyFtD03DGba8myZ2sDlM7hhquLzG0oI3oeKZM2DS4oCVGtuhwIlshHjZZ1RJlIFuhvzqr8aYrV055cznZrMglK5rY3ZOmaHuYukbMMijZ3gT/itOF70ue2D/Iru40zRGD45kSFccnbOpELY2Rok3Y0hkp2mTLDolIkBCTr3hYhiBqGZiaoGi7jNgeRdvj4GCRfNnh0FCRZ3pnNkuFk+8pJDRFDFxP4njBRWTj6xkygrjksKETNnUMTdTTanpGS7Q3hTh4osBQNTlpJo5nSly2cuwF2HQXpI3KroFcpe4Z400iR9c1gev5FGyvfhE+kKvQkYrMal+NzMXFH1CO/wrFEiVXdoMJCE6mw9SiY+t/r1LOqW1zpv01TpA04vuyPkMgJfW2zhqGFvS1FB1v2kmZum9G9Wa/8XHXl2gECTF+Q0vuVEhO+oTVrjuklOhS1Ldda3G1Xf9kwaXaMFNrUXUbihE+Y4+tFnHsVdPsQoYWKEINgd8wjhq1bdbGXbK9Ma/FYK4STHREgu9uz6spW6c50Oq4qMUtEyhwGjnXTUg3dibZ0JFQPlgKheKsQhVGTgOz7bm8bm0bP3tpcNa9mVd0JukeKVKoOKQiFroGJSdIZKnNoLuGhuN5jBQcNlWr9I0u+E3hk8oJKSXxsIFXNSmbKSp1MgQ+IdOkKWzgS8mxBsO0qZAEfdLJiEWu4lB2fAQSVwaP12ZyXF9iVB8/ub8ATQs25FYLJbLqJyLlSfVMPGTwa5e00xK1eP5YlqLj0RIP0RK38HzJs72ZQCKsaQgh2LYzmImbanZj/KyIlHBha5TBXAXHk5P6V5wuah4Uu7pH2Xs8V1doCCGohhHhS4khgovFPcdzbO5K0d5kgZS0xUIUbZd/3zvIaMmm7Mys8KhRa3HSRDDTZ2pge8Fr43k+IUOn7PrETA3D0AlXY3OFENXXzSdXDt4HFTfoC7d0jbCpU3F9vFlmUAYKqIlMdUHaqOwazJXx/UC27YzbX62nXcqg4NZ4ET7bfU2139mMVzn+KxRLk6B9JvjbrLVp1iYt6qo2IebUtjnT/honSMyG2FpNE/VeU1FVNzTiBgYZRE192kmZuspEBC2pjY8bWvD9Iar786oFi6lufQUno3VrBRDPH7vtWsGkNn7RsMVai6qhi5OeaIw9tnrEsSYImzqmoVU904Lnwtjx1bZZe3kilj7mtWhvCmHooto+qaHrtQLNyWuLydAAr/qdJoBoaOxn9vlgQqppQhXoFQrFWcW5+4m8hJhtz6VhaHPqzbz1qk52do9ycLDAQLaM7flj2k1MI/iyf/LQCBe2xbiiK0nMMugdLfJSfxbbdRkuOJTdoGfZ0jQ6W6L0Z8r4uoYmgksFx/WZ/BZzLLoAKbTghlcTFG1vxo4ajeDiomh7GHqQAFJ2AumxAAxNI11ysB0frSp7DWsaZcerzwxpQhCx9GBmy5P4MpDaVq9P8XyJL4OL0fXL4xwaLJCvBOk9qYhJRyrMvv48rheoVjQBJ3Jlvv/ccV7sz3L3TRumLY6MnxXpSkb45eHhCf4Vp4tGDwq7GkcrCQoVSFmd3QsUGqmIQcTUiVkavSMFyo7HQK7Mi8ezVGZZGNM1gamJMSoOy9DQCGbKyq6PZWhELI2WqEU0ZJCMBu8oxwvGly8ExRd7CrffmDW3oghA2Jy8eDDVBWmjsits6lWvGzlRmVH7r6CuaqldhM92X1PtdzZKMeX4r1AsTS5sjXFFZ4KekSJF2yURNjH1II2m4gTeS5om5tS2OfP+GiZIoha1TyhDq37mE3w+jf0KkhQqDoamcUVnctpJmXgo8M8SQNw6+RnXFDZojppkSja6rmEK6pMpUylFDV0QMTWklBRtl5aoyWjRQROCuKUjhDjZ4hoyAlVhtYjvS+otqjFLZ6RanDB1MaYgZGgS6fsYuhako2knt2npgopHtT0nuDZxPR/LENiejyYEmzqTY16L69a2sTIZpnu4SNjUiFSvbxwv6JkZq1Stnu9qocetBGa5li64qCV68uwrE1KFQqFYkqir59PEbHsuxz+vP+PhAyuTEd64qaPuM/B8X4bvVnuLLV2QLrn1L/vghk072TrjeBxLl/jWr3oImRqDuTIv9ecZf4tZxqPQn0My8cKmdt0x3b2yrEptj2cr9YuOmW6tDV1gGTpF2yVddNA1sPRgxsb1fUq2R6lqXaET9FqvbA5TKLv0Z8vVNgdJ0fZIRAwuiIcYyFUCxUv1IExDpz0ewvV97n/8UNXJX8c0gtaj/SfyFG2PmKWTjFoY1RmrQsVh7/EcX/75If7v7ZunbasZPytyJiN5G30yfrinHwjeF4aAkBnMaEarF6GO5zNadDmeLWNXJcKzRRdUb8JNTE1wLF3Gp5YkoFWTWwxiVmBC5xPcFAznK+QqbrXQMbsdlhyP53rTZErOrNOOOpKRsRJ2pr8gbVR2rW2LBZ4xZZewqU0o8Hm+xDSCC/R00a5fhDcy24vfubr4K8d/hWJpommCt2xZxd7jOfb250gXbWIhg7ChU6wE6remsMHq1hiFyqmbUWqa4LYtXbzYn2Xv8RwjBZt4KLisK1Tc4HtODxQX6aJDrLosXwmuF9Yvj3Hblq4ZJ2XWtMVAwIHBwphlpqERCxm4fu26wx8TKdz4uSkICua1ArihaRi6xtr2+Jht11tccxXiIRPP9ylUPMq+xNAFYUMnU3axDB23qiQtVdsea9HrsZDBqpYoB4fGbrNQValIKSm7fl3pAgLXk1y6Is5tW7rGvBaGofHe69dyz6N7OJGtkIiYJCMmwwUbr6pQDZvaGANWs5oS5FWPu60pRNGRRIRUJqQKhUKxhFGFkdPIho4E4VetZm9/jsFsmfZEmEtXNHFhS4xDg/m64mBDR4INN23g3/b08+izxzmeKTOQLfP3Tx7lPw8Ps3lViu8/e5yRgk1Xc5TliRBPHh7Bdn1ChsZlHU2kohYjeZtDVV8Ct3pR0Tta5PBQccoxTnXTOZubUZ9glsoNhBvT9hrXsD1JMqrRHo8wmLMRmqAzFUYXgsPDRQytdmFVu5jxKFVcUlGLZMRAExqelBiahuf79GcrbFyZpClskKlWVJJRE9fz+cWBISqOz7JEiIhp4PqSdDVetroLjKrs2dRFcA4LNk8dHeHQUJ51y5pmcUSzx3V9th8cmpOyxLY9tu3uoy9dojMV4eaNHTzVM8pgrkJr3EIi2dU9SnM0xLF0EG/bEjMJGTohU8eXknzZ4XjGCQx7T2H8QgSFsHw5uBBtawoRNTU0TQvMXGXgzl+oOJRsyUixUm2b0ak43qwLHAA9IwUkIrCfmcTUbzyrWyIsS4RnpbzyfVlX/Fx9UQu9oyUODhVY0xbjhb4MJdurx09Lgve3IFCxDOXtCRfhc3Xgn6uLv3L8VyiWLhs7k9z9xg088PODPH10tP49lIqaNIWDm+pM0aFsegtiRrmxM8ndN23ggZ8f4umjI/X9xcMGr76ghdddtox/f3FgzFjiYYNXXNDCu1+zZspJmfGTN8CEZVevaeNlq5L17Xu+RDZ8ttdUdRFTpzlmUnZ8io5HSNfoSIa5cnXzhG1XHJ+OVISK6xEygjbaE9ly4OdEUCSPhw1es76di1fEp4xeHx+h3pGK0OIGbbODuQp5O5CO6FXT8fHno5FaG+z9jx/kWKaM60nCho4nfXRNQxOCkHHSj8XxJZqE5qjJGzd1oGuaMiFVKBSKswAh5VzmiM9P8vk8W7ZsYceOHcTj8+uXrHk+7O5OcyxdouIFRYxU1EQXgpChY+qB+eS6ZfExxY/Gm59j6SKD+QrxkMGmrhRCCEbyFXZ2p4mHdHKVIF1l86oUu3rSnMhW8HyfsusTNTUy5dk0xcwfvWqoOZc3VWvMJBYysV0fxwvGl6+4YwzWaj8EPhmBX8jyRIhc2WuI2hUM5St0NUfZckHzGLO7/zw0TM9oEU3TaI+H6m0P+bLNiZwdSF91jfamcN0RH8B2PTIlh7+67Qp+84qVp3RuGnlkd9+YiyxDF6xMhnnv9Wun9CL5/GP7efCJw2TLbmBmWsXUgwszT/rVi1CDZMQI2qpE1UyuEvhYeKf4565rgpAR+H2EjUBW7CMQ1XheR0ryZRfP83GraQIRM3hfj+Tter99ZRbmqY0Y4mRxbqYjiFk6t79iNW+6ciXbdvXVL4xDpsb6ZU1jLkhrf5eNz2mOBtGPo0WbY+kS/Zkyjh8k39QULoEaRqczGZn0Inyyfc3EZGOZahtzea7i3GUhvpsUY1moc+r7kkNDeV4ayAOSi5cHkyDdo8VFMaM8ub8cILh4ebweOTvZWGrLJtvOVIaZUy1r3L4vJWFTcHS4RMX12NCR4NqL2ujNlMiUHLIlp666mG7bq5uj9XMVCwVF/QMnChPGP90Ew1TbzJQcMiWbfMVDE2PP1XSM39erLmjhyaMj9d9fsaqZ7z1/vD5x8ebNnViWPu05VSgUiplQ3/WnD6UYOQ3UPB96R4sMFxw8KYlWo+oOD9romiARsdjUlSRs6Dzbk+bfXxwYU/yAYJZneTLM/hN5dKHVFRk1czFPQtQyyJRc+rMVhvM2FTeYvQkib2v+8bNjJn+QyfBlPVBm1mRKDkU7iBsOzCwlrj/WWE1o1G9MfQnZsosmIBE52fqSKzvBbFC+Uo9CBMhV3OrzBboYG90rRHABFbQPyTEFh4lnY2F4ZHcf9zy6h5LtkYiYhCIaFdene7jIPY/uAZhQHPn8Y/v53E/24/oSy9CQPmM8QGKWTsQyMTWNsuszlLcp2t6YZJ750JgUEzI1jKoVv+v72G4QO+l4QcJC7fWRBP42zZEgrrLkBOa/smpA6Mu5FUUgUCOFzJO+JXZVtqxpwevpS0lrLMSr17VR8WQQ6xsy+NhNG6a8IG30YulIRogka8XHEs1Rk7e/6gJWJMNETZ3edJHhvE1zzERDMFywJ1yEn6oD/1xc/JXjv0KxtNE0wbplTROUhotlRjnV/mZaNtlzpxrjVMtms/2ZjnuybY///eLliQnrTRVlPNttzoXJ9jX+99tfuXpW41AoFArF0kMVRhaZmufDcD6IfPN8n0TYRIigB9iXENYEnu/TPVzgytXNkxY/arhe0DZStF1yFZemsElT6GSyTFM1VaZsuxRtF8+Xgcyzang2F+Za4Jgvng9hs3bTHES51naraYF3Qm0cWoNyoNHp39Q1mqMWRdulUHYp2w6DuTJFOzBp9bwgoUbXtDFGbZYh6rGxXtUQznY8DEMjbAgKFZd42ODi5VNf1Ixvb6nNEk227OaNHdz/+EFKtkd7k4XrC2xPomsa7U0WgzmbBx4/xE0bO+o33Lbt8eATh3F9ScQM1CGyalIXNoNWopLtMZSfW3rMeHRNEDY0QubEpJiK65MvB74gNW+QmjpI14Lz6tciokVg7potO6QiJiEdRqvPb28KMViwmWvJTVSLe7Wfqb43JdAWD+F4ks2rUpimjqbLejJL4wVp46xdLKTz8I7Ai2XdsviY4uO6UJwDJ/I8eXiEu2+6DE0TrF9xajcUs2Uu21AX2wqFQqFQKBQKxcKgCiOLzJHhAgdO5ElELPrSZaKWUTe9tL1g9t/xIa5rZEouubI7afGjhqlrmIbAcSVOrR1BCNa0xSlWMlUD00A9UosS1TRBU9ikMkV86VTMtygi5ig10aru+bYnCRk6vu/WI3mn67ioRbpahkZTyCBk6jSFTUbyNo/tG5pg/mpowb/GkJGaoEISFGhGi0EPdpB2A5ah8+r17axpm/wGdHx7iyYEn/7hXt716osAJiz75Pf34HqSprDBSNENfD6qBqFBcotOX6bE9oND9Zmobbv7KLs+bXELSxdAoMLIVRyG8vMvhAQRuFrVf0SrxzI6nk/F8clXpk+K8atmv74MihU1tY0nARmofgZydv35mhBVg1c5ZzWST9BbXluv9t4MInODPu9a3ONkySzjW0+8qlHpuvb4xNQZIehIRth/IseR4YIqPigUCoVCoVAoFOc4py9DdI6MjIxw44038uSTT455fNeuXVxxxRUTnr9t2zZuvPFGNm/ezK233squXbvqyzzP49Of/jTXXnstV155Je973/s4ceLEoh8DQK4ceDvoWtBmYDT008qq8VftRtHzg5aEevHDayh+VGkKG0RNHccPtpkrO4zkKxi6YMPKJkw9mOnPV4IbfE0TpCIGgrGRdotFcJxzXEdAxfURInB3n+0Ns65V13U8Roo2ZcelUAlSfGpD0DjZBOP6wTnOlBwcz6fseAzlypOagEqCG3zb9WiKGBwZLuCPa0uptbekSw6aBiFDoGmQLjn89Y9f4v/3o5dIlxwMXRA1dQxdBO76rs9o0aHieMiG/1Ucj3zVC2QwVwnGISVHhvJELJ1CxeNYpkJvusRwYW7qkNq5TUVNlidCXNAapbM5QkvMwtSDaOUT2TLdI0V6R0sM5itky+6URRFLF4iqeseXIKU/ZURjDdeXZMsOnh8UVPQ5fPrUNy1OtnjVCiS2K0lGAuVULZll/bKmejJLrWXmud4MqYjFhW0xoqZOtuRy8ESe0YI9YX8RS6fiBKoThUKhUCgUCoVCcW6zJBUjO3bs4CMf+Qjd3d31x6SUPPzww3zqU5/CtsfeyDz55JN88pOf5Mtf/jKbNm3im9/8Ju973/t47LHHiEQifPGLX2T79u08/PDDNDU18bGPfYy7776bBx54YNGPpSlsEDI1PD9oVXB9iamLugmlV1ULSKgaiGr14seQbWNMUsyIWDoxx+Dpo6PBzWi1tUATgovaY/zBtRfiScnf/PQAA5kyI0UHpDONf8bkzFWJEBR35rgSQduFEBIpAsXGbG0xfBlkExt6oDYZyleoVKUmUVMghI5EIhD40qPkSAq2R0vMoux4pEs2thsUpRBBkUXK4F/VtxQJfHdnH0eGiqxfHue2q7rY2Jmst7c4vkSvHrcnA4MVDeqKl6guMKoVAAuB9H0qXlAkGOv/IQnpGtGQQcgQ7OvP8sF/2Ml/HBxmeJIb98kQ1X8+YFZjDUNVRUjNUNaXMjj2okPF9ai4/ryUQU6jMS5Tv+61d2/tbep6EiFqqp256Ubq7/UGJKDrgtUtUfKTxF82xhc3tswkIibxkE7R8Tg8VAgMVxuUI5OpThQKhUKhUCgUCsW5yZJTjGzbto277rqLD3/4w2Me/9M//VMeeugh7rzzzgnrPPTQQ9x0001s2bIF0zR55zvfSXNzMz/4wQ/qy9/97nfT0dFBPB7nox/9KD//+c/p6elZ9OO5sDXGumVxsiWbRNigaLtIGRRHLF1guz6mBrbnk4wY9RuxiKXTHLUYyFaClA8/SPsI2nJM2uMW9dvOhtqJkNDZHOXXL+9gXXucsuvhVc0xnbnko07CTHqTSe5bZ0SDemEIaIjnnZmQoeH5MrjZ5mRbjC7A0HV0LYgK1DUwdR1LDzxJTEMjGTUxdZ2WmIlejROMWEE7jlH1HUGAoQdtT47r81xvoDx4vi/Dtt19QfShrNelgmIXY6ON/XFnzdD1+s9CBKaprVGTlpiJaWhkSw7HMxW+/IsjfO/Z47MqigggYupVNUiYC1qjdDVHaWsKETJ0Kq7HYK5C72iRo8NFBrIVMtql3v8AADp/SURBVKWgDWm+7VLjV5tuM41nwNBPFtB8GfiOzGY9CFQm49EFLIuHyJQc0iWbTV0p7rxhfT2ZpdbK1pGMjGmZaQobJCImAkgXbXKVk8qQyVQnCoVCoVAoFAqF4txlyU2Hbt26lZtvvhnDMMYURz70oQ+xYsWKCa01AAcOHOC2224b89i6devYu3cvuVyO/v5+Lr744vqytrY2kskk+/btY9WqVYt3MAQ3y7dd1UXfaImS46FrGtmyg6VrVZVHkHkf1TRWt8YoVGe9u5qj3LSpg909aQ6cyDOQDSI5r+hMMlyocMyTvPaSdvIVr95+Ew/pHBws8N2dfbibfXZ2pynZc/egiJkadrXtx3b9aqoNpKIWlqExki9jVw1Sw4bG6pYwR4ZLOF4Qm1tLJpkJnSAjR5OynqZhu7MbrSAw40xEDYQIWpOGqkWExrjdRkxdw/U9rl3TyqauFF//5VHCumBXbyYwGHV83GrqTQ0NgSsDlcO69sCU87s7+4iaov68xiSQmhqiVnAYr9IxdUFT2Kifp7LjUbDn5v1iaoKIpU/wBvF8ScU9dTVII4JAkeFVlR41w1OYXYTu+GKZpetEIjq5sosn5YT2JEFQjNKrSTN2dQeGFvi9UDV49auvU1NI547XrWNVc3TSZJZaK1skqY/djxBc1BYnX3HJllwyRZuoZVCyJ6pOFAqFQqFQKBQKxbnNkiuMtLe3T/r4ihUrplynUCgQiUTGPBYOhykWixQKBQCi0eiE5bVli83GziR33rCeh3f2srs7zbF0iaLjETYNViTD6EIQMnQyRYey6bGpK8WtV3WysTPJzZtWjonk9KXkE9/bQ0cygqZpJCJjiwAdyQi7ukf5jwODDGTLdR+H6UxMa2gEN6WWqXNxa5QbLl3OvzzTh2XolB2XfMXH9SSpaIiopZOKWmia4NYrV/JXP9xHIhwYYA7lyjiz2F/tltiTgREogOPNXCQIiiJBgkjF9esms1HLIFd2Jr1JlzIwFQUwNI1Nq5KknjGx3cDQc6oigu356EIQNvUxppzr2+NBnPAk982NDwktiJkVBO0nRTsoWMxEU9ggZGgsT4TxPY/+nF0vhGhVXxrb9SnZHqOOXT8PNTPUhSJcLbw4mqx6opwkZOqEDI1MyZnUp6XWKFMzspUSYmGD/397dx4mVX3uCfx71tq7uptegAZU6IYEAWlQwBVcwHiVmIDGZJDk+uTGO4g6cRKXRCLJoMRorkbNaKI36Khkc4+jk0vM3KhRYVQiQhTtZgnQLL1X13623/xxqoqu3mjUXuv7eR6ehz6nTvXvnD7w6/PW+3tfCW7wyrAcQIYbdMkcoykSZMldZqbKEozM/aArSm6ZkyS5gSFdkWA6AiGPilMmFvc4/uxStqRhI9hlWUxpQEd1eQj1TVEkTQd7m+PwaHLevz8iIiIiIhr9hl1g5JPw+XxIpVJ521KpFEpKSnIBk2Qy2W1/IDB4afIzqsKYPq4Ie1viiCRNdCRNFPk0hH0aJpX4sa8tkQt+dP7Uu2tLzm3723v8BDzLp8loaE8gmclC8GruA6WT6ejR9QE2k7gCR7hLU4QAUqaDCSUBnFlThtfqmnFiWQCy5H76ns1OcYM0wN7mOI50pCGE+xAqy25NDdM59sN/9hU+1V3iYmee6PtTeUKWJSiZaySEu5xmQokPjnAQT9tQZfcBGwCMTIvZ7Hs+s/UANu9pQXnQA8t2MhknvYwx0462ssjjjlVXcKTDQc3YIJRMxoTsCEidIiSqDHgUGRLcmjJN0f7VCFEkt86MT5XxtXkTsaspjoljAvhHUwxx82htkJTZc3BlQliHR1OxqzkBGUevb390fn1AAZJOpttM5hqqigTDQu4HI0tAsU8FJBkJwwZsJ3dvyZl6N4D7s8kGasJ+FSU+FSlTwLTdII4EIJa2YDlA0KOgJKDDcdyfr2XbSBo2JAmoCOmwhZTr7qMpQCRpwqMqKPId7drUVXYp2/YDEVR78jvQiEx75gunj8WKBZMQT9s9Zp0QEREREdHoNioCIzU1Nairq8vbVl9fj3POOQfhcBiVlZWor6/PLadpampCe3t73vKawdA1yNFZf1uC9vUJOAA0xdJImg4kSYYqu+sepMwn8D2lRAgHuUoztiPgCIGgR8Mls8Yh7NNy3yugy4gbFlKGDa+uIORRkDTc5T1VxT6oioSkYcEWDqz+pKd0IkmAKjkQkpRr9dtXYMTtRiLQkTTh1zM1WTQZsiyhxK8jYSQRS9tQ4D7sd36vYp8Kv65iX0sChyIplIU8xyxKazsCB9tTqCrxoSmahmHbkCGhqtiL/W0pWALwSm5dEssB0rboVxVaWXJrgwR1BT5dgaa42SCyIuHz44qwvy2JjoSJhGljX2vimO9XGvLCqynY3Zw4rpbJigQoigzLdqDKEkIBD4xYCo5wM2YgZADucqdsdoemyFAyWRzZVsPZLjluQCVb9NYdhl9XsPSU8fg/2w8jadgo8mnwqG7GT8KwAbjLZqxMC2vDcnKtpwO6gphhw6+r8ChypruNBU2WMS7sRbiPwEjnpWzZWiM+XclbMrN87gRUV4T6d7GIiIiIiGjUGRWBkcsuuwyrV6/GRRddhLlz52Ljxo1oaWnB4sWLAQDLli3DQw89hJkzZ6KkpATr16/HvHnzMGnSpCEe+fE71ifghyMpaHImwAD3AVNT3Idfy8nPxpAzX2Sf4bOf4s+qCuP8z1Vgb2scYZ+Gv+1rRTRtIWk4uU/s/bqMkoCOc6dV4suzq3Df/63Dwfb8rJ3+ipvZEfV/DYgQgKrIcAQwJqBjTMCDplgKbQkztyyj66IcXZEwJugF4C4RaexII56yes0WyXIEUHckiiMdSRzuSMOrKfjN2/uRstyH+LTlINWPYJCU+b4lfg1eVYbPo8K23e40huUgaTmIJU1Ulfhx0cnj8N6BCLYfiOCkUl+vS2SyP09Zcmu2nFjiw3v72nO1OfpDU2XoiowU3J+tBLf1rgS3lkfKsSEEoKvILV/xqG77Z3e7jFQmIymbxePWmXGDKEVeFWu/OB07GjpQHvLAtB1EU7YbvJIlnDAmgFgm6JYwbHSkLKiKhBPLAigPetEUS+WOSTpuHZtSvw5VkVA7qeSYBVI7L2XrXK+HS2aIiIiIiAgYJYGR008/HWvXrsUPf/hDHDlyBNXV1XjkkUdQXFwMAFi9ejUsy8KKFSsQj8cxf/58/OxnPxvSMX9Sx/wEPKgDcDNH/LoCyzHziqJ2frjO/r3zA61XU9CWNPDfn3oPbQkTe5piaMgEPDRFcjvBCPcT+4RhY0xQw0t/P4QjHZ8sKPJJCbjFUMdklsP8/WAHUpYNy3Z6radi2AKHIonc8iJFkdCWMPq15KQplkYkZUIId+lHYzR9zGN0RXIDILqMkFdHeVBHyKOgLWHiYEcazc0JWI4DKxNsciyBcFDH1QsnQ9eV3M/5wyMxeDUlk1lxVHb5iwSgssgLv0fD7pYkJpcF8HFjrF+1RlTZre+iSEDYq8GwHDRGDUiSBC2TSeRk2hD7NBV+j4LmaBqmLZA0bOiqDF2REEs5kGUJM6uKUBnyYn97Au0JE5UhL9YunY6QT8PzfzuIKeUhBD0KomkLpuVAU2WEPCpiaRut8RS+eEoVFFlCeciDM6eUYeeRKO7/cx1a4wYmlKhQMi2uoynruAqkdl7K1tOSNSIiIiKika69vR3r16/Hq6++CsdxcNppp+GHP/whKioqsG3bNtx+++2or69HSUkJVq1ahcsvv3yohzxsSEJ82r4Vo18sFsPcuXPx7rvvIhjs35KXgbajIZL7BDxtup+A11SE8OXa8Xhm6wG8vP0wHCHgUWXE0rbbXcZx2/ZmC2IC7jIWWZKgKzLKQx6ML/biw0NR6KqM0yYV46+7WxBNHW1lqsoyFNmt8WDaAmOLPGhLmGhPWj2OcyDVlAcw94QSbN3fjta4gYAm4R9txw5Y9JeSWdrT7y47EuBRFQQ8KgIeJVckNWXZSFsClu0gbdkwbdFr0GL2xDCeX31W7usdDRH84tVdeO3jJli2g6R5tFZKtohpTWUQfk3FV+dNxPaGCOobY9jdFMORjhRsp/c8nGK/hqpiH9oSbg2UYq+GplgasbQFn6ZAALlaJroiwRbApBI/LppZiZe3H8bBSAqWLaAqbgbHxFIfHIG8+zGbkbFtfzvueOlDnFgWyAXhOrMdgb3Ncdx68ee7FVLt7V5ntgcVuuE4N410vKZERDScHO+8tHLlSoTDYaxfvx6yLON73/seDMPAXXfdhSVLluD666/HFVdcgbfffhurV6/GY489hlmzZg3CmQx/oyJjpBD1Vsw14FGxbM4E7DwUxc7DUTiOjZBHgaXJiKcteFQVVWEPHABhn4qYYWc6zeg4qdSP9xoibstZCOyLJBHLLGtQIGW6ugh4ZMB03Fa+e1qSxxrqgNnTHMeepjgsuEGCeM9devst2z0lWxfD7tybtgeK7GbQBD0K/LoKSXKvUdp00BIzkMoEQY7He/sjWP3rd/E//8tcAO7P+dsX1OBwJAWfJiPk1dAcSyORqblxUpkfCcNBe9LA7InF+NLsqlxWhEeTsPUfbZlAF/DBwSjaEiYmlfhx17KZaE1ZiKYsBDxuId+Pj8Sw4a97MLbIC0iAaTlQFAmJtOXWDxHuspt/mjke//WcaryxqxlN0XQuu0OWpR4zMizLwc5DHWhPpPGPFuCEUh/ihpNXyDdp2PBo7t+7YrYHEREREVHfduzYgW3btuHNN9/MBVHWrVuHpqYmbNq0CcXFxVixYgUAd8XF0qVLsXHjRgZGMhgYGcFkWULCsPGHbQfzPk2vrgjia/Mn4c8fHsE7/2hDRybjI+TTcNoJpVg4rRw//7/1qGtMIGHYmbohSXx0OJqpg6GgI2WiIxWDLQDbEshFCGxxXPUrBpLVaRgCbieV45VtI5s9w75OTZHdpTFBr+q274WbUZG2HDRG00iZ9mfSKvf/bD+MRMKE3+8WFZ1cFsTMCWFsPxBBVYmGsF/PvVYIgUORJGZNKM4FC7KFfHc0RFDXGMeOhg4cjCSRthx4FBk+zcB9f9mF5XMm5GVnxNO2Wz/Go0KRJbTFDdQ3xtCRtGBnWgFDAt7b347J5UEsnFbRbexdiwi/8F4DfvnqLhxsTyFuWKhviuNv+yT4NAW6qkCRJRR5VaiKhAWTy3qtF9JX4WIiIiIiotEsFovlfa3rOnRdz9v2/vvvo7q6Gr///e/xm9/8BslkEmeffTZuvvlm1NXVdWs8Ul1djaeffnrAxz5SMDAygu1oiOTqL4wL++ALu7VGth+IoKEtievOq8a158l4o74FSdNCdUUQjR0pPLn5H9jbEgcAqLnWqg5iabfoZiQ1+MtiBlPnArR9BTJUWYJPVxDQ3U5AbitjGynT7ZjSU8vcz4IjgJ++8jFu++LJAPrXWaVrrY3svXGgLYHWmAHLceDXFBi2g4ORFNK7W9DQlsT159fklqN07nhk2g52NESQstzMFFWWkDJtxNIWnnn3AKZWho65jOWF9xpw+//+INeFRlMkNMcMt/uMY2GM6nbhOdiehE9XcMrEMLNAiIiIiIi6WLhwYd7X1157La677rq8bZFIBB999BFmzJiB5557DqlUCjfddBNuvvlmlJWVwefz5b3e6/UikTh258tCwcDICOU4As9sPYDWuIHqiqPdaYJeFdWeIOobY/j31/egNKCjvimGPU0xHIqkumVEGAI4nm4wo0FvZ5sNhPh1BV5VgeUIpC03GNASP/5lMZ/Gnub8qHCus8q7B7C9IYKEacOvKZhZFcbyuRPyghSd7w3Tdou7Fvt1ABK8QqAjZcJyBFpiaTy7tQHTxxVBlqW8jkeRpLsUyG2F69ZKMWwHY8NepEw777ieWJaDX766C0nDRkWRB5IkIR6zoCkyHMeBLYDWuIGyoI7xmXbP2/ZHsHTWeAZHiIiIiIg6efXVV/NqjHTNFum87dZbb4XH40EwGMS3v/1tfOUrX8GyZcuQSuU3y0ilUggE+u7uWEgYGBmh9rbEc9kDnVv2AoAkSfDrKv5a34xxxV5YtoOG9lSBhT+OLRsI8WkKPKoM03YLpUaSJhqtNIayLHEkYfa43W2CCyC3/Kf7ILP3Rsir4kBbEn5dRbbcbvbe6EhaqCr2o64xir0tcUwuD+YyUz4+HMWh9hS8ugLDciBJQMKw4dEUnFQWhK7IecdlOY7I1QHZeagDB9tTKPJpkCQZpu3AyLQ3liUZhu3AtgVOGBPAlPIgYmm7x/ckIiIiIip0wWDwmMVXq6ur4TgOTNOEx+MBADiOm+H++c9/Hr/+9a/zXl9fX4+ampqBGfAI9CnLVdJQiaYspE0HPl3pti9bdyJtOagM6djVFGNQBG4gJORVUR7yoKrYh9KADkkCIkkT+9uSONyRQnvCzBUaHUonluWnumWXxuxo6MD4sA8zqsIYH/ZhR0NHZnsk99rsvaFKbotmtUsGRrZ1syK7HWSiXZZOybKEtO0gkjBxuCOF5piBgK5ixvgwSgM6fLrS7bgdDRGse+kDrP3D33HHSx/iV3/djbhh5bofOY6AEALZGJ4qSxDILOOSpB7fk4iIiIiI+ueMM87AxIkT8f3vfx/xeBytra249957ccEFF+CSSy5Bc3MzHnvsMZimic2bN+PFF1/E8uXLh3rYwwYDIyNUth5EIm2hI2miJZZGR9KEEALRlIW2hAGvJqMxasK0h3q0Q0ORJQQ9KsqCOsaGvSj2axACaIsbaGhPojGaRkfSGrBaIZ/G5PKi3N+7LpsKet3iqEGviuqKIFrjBp7d2gAnUzAle29YQkCRJVhdCqm4QREJtoO8TjDZ4Etb3ECRV8WYoI7SgA6PKsOynVyQo2sHmexx2w9EUOzTcWJZACUBDxwh0BxPI23ZkGUJkiTlAk6OcIMi3kxgr6+uNERERERE1DdN0/DEE09AURRceOGFuPDCCzF27FisX78eJSUl2LBhA/74xz9i/vz5WLNmDdasWYMFCxYM9bCHDT6FjFAnjgmgxK9h8+5WyBJgO24goMinosSvIWU6GBf2whnq1IdBpEgSvLoMn6ZAlgDLcYultsaNz6RbzGDxqBL+5cyTcl8fa9nUuLAvbxlK51ohIa+CtoSZVyskYVgoDejoSBo4ZWIJThwTyAu+nDy+CIbtoDVuoMirIehxs2r2NMdR7FPzuuD0VuvmxDF+fHQ46nY3ShgoD3mhq5KbyaK4rZ6DXhVjQ55unXWIiIiIiOj4VVZW4t577+1x38yZM/Hb3/52kEc0cjAwMkJ9cKgDjdF0rgZE0OP+KJujaRyKpKBIwLhMoczRSpbc1sJeTYYiSTBtBynLQUvcGPKlMJ/GBdMr4e2UOZFbNhVWIIRAJGngSCQNyxEo9muoCHnylqF07mKTMCyokoT2hAGP6nalUWUZqixhTNCT62azuymWC77IsoyTyoKIpyPoSJnw6yp8moKWeBo7DrlLeXo6rnPQRpZlfH5cEd7d14aYYcObNhHQVaRMAwnDhqbImFYZQtxweu2sQ0RERERENBgYGBmBsp/SW7bA/JNKsacljo6kBdsR0BQZquIGSuJpC1PKAvjb/rZhvZymc/vcPl8nAT5NgVeVIcuZQIjpoDXec6HSkajYp6Iy5IPjiFyQILs05nAkhY+PRNEcS8HOrP6RMsGhiSW+vGUouS42Ww/gvX3tOBhJImHa8CgyxoW9qJ1UgmVzqnLdbDoHXwCgNKBjRlUYe5pj6EhasBwHhuVg8pgArl44pdfjOjuxLABHCLx/oB0JwwHgQFdkBDwKyoMeCAG0Jw3MmlCcNxYiIiIiIqLBxMDICLS3JY76I7Hcg/CU8gBaYu4n8X7dfeg8GEnBqynY3ZJAVdiHva3JIR71UV0DIb0FRSR0ygiRJViZjJDWXjq2DEcqAK9HhWU7sBwBGYDRZV2PnAn46ArgURX8bV9bXneW7LKpP33QmMsAyiZWOMLtGLO3JYH3D7TndXSZURXG9HFF2NsSRyRpoiNposinIezTcOKYQF52Rjb4kjRsBDP3VWlAR4m/BNGUW8cmYdq4/oIaVFeE+jyus4qQF3MmFeOLp1RBkSWUhzw4/aQxOBBJIpqyEPKq3cZCREREREQ0mBgYGYHe29+OjxujgAASpo2kYeWKWcqSBJ8uY0zQg3M/V4En3tqLppgxpOOVJUCIowGQvrJDvJpbI0RVJNi2QMpyEEmaI6pGSGcO3CyQaNoCTAeyDBiGezIeVYZHlWFYDlRFRmlAR3vSwMFICpFkfvDHEQJpy4YAoEjuchkhACnTt9d2BB5+bTcumTkeqnq0prIsS/1qf9u5Lkm152itEElyO/kc6UjhlAnFmFwW7NdxAPJqh1x+6sS84Adb8hIRERER0XDBwMgIs6MhgmfePYCkYUOWgHjaytXTEBCQZAmxtI14OoH/9cZutCQGt/2pBPdh3HFELgDSV1DDo7qBEE2VIBwgZTnoSLnLgkYDRQZsARiWgKq43V2yJLjBA1VxgyOmI+BRFSRMGx2dAiN7W+LY25yALEmQ4F7X7PVRZAlqpv3ugbYk3tjVjIXTKo57nJ3rkmRrhvh0BUnD7rMGyCc9rjPHEdjbEmcGCRERERERDQkGRkaQbG2RlGmjMqRjd3MCQrgP35LkBiMcIaBK7nKNwQiKKFLmewsBJ5MV0ldQQ1dk+HQFHlUGIJAyHcQNC2ZydARCupIlCYrkBkAcAZidAiOm7cAWEjRFhhACtu3AsB14FBlFPi33umjKQtywIYSAlFmHJOAGViS411+CgOUINEXTn3isneuS1DfGcKTDgUeTj1kD5JMeB2QCfZnj0qZ7XHVFEMvnTGDNESIiIiIiGhQMjIwg2bat44v9ACQ4IpHZIwHCfUC2bQFZlSEJkffw7PT6rsdHlSVIkhv8cISbDdFXCxhNkeDTlUwLXQkp00bCsLstFRmtHAhE0zZM24FjHV1G5GaLZK6jY0OWJSRNG7qqYFzYm2mv6wp5VchSJvNGuFkibljJPd4WNmQAnkwNj0+jc12S48ng+CTH7WiI4P4/16E1briZJmE302T7gQga2pK4/vwaBkeIiIiIiGjAMTAygnTuAKIqEmT56NIK4RacgCQDAV1Bu+30r9VLH2QJ0BS3XoXtuBkJ1jGWuCiyBH8mEKJllogkTRtN0fSIrRPySQU9CtJm9yCQrrjBgqOBDUASAmMCGjRVQe2kEpw4JpB7/aQSf+4YZI+UJGSqgMByBBwJmFDsw5lTyj71uPtbl+TTHJfNfmqNG6iuOFqbJOhVUe0Jor4xhme3NmD6uCIuqyEiIiIiogHFwMgI0rkDiFdToEiSmz0gychW9BBCwKMpkDo9jGeXXxyLrrjv5z5sOzBtgbTVd65JtqOK36PCq8qwHIGEYaM9YcKwP6s8lZHJsNz2tH6PjHjaQjZWZTnCzbxBpkYI3O47uqqgqsTfrSbHvrYEvJqKIq+aqb8CyJL7A3XceBgUScKX50zIK7w6nGWzn8aFfXkFWwE382lc2Ie6xmhedx4iIiIiIqKBwMDIMNe5MGXAo2BKeQA7GjowucwPv64gmragKzaEcDMHPKoMr+p2p7Ezy2nsHoIisuQWPlUy3U1M24Fhi55f3IkEwKPJCHpU+DQFkICkYSOettAcs/taVVNwDFtgfNgD05GQNh3oCmDaAmYm88ajypAkCboiwRHA5PIQ/nXh5G7LR6IpC5oiY/5JY7C9IYKmWArZmJMkAX5dQUWRF/NOKh2Cs/xkOmc/9cSnKzjS4SCaGtziwUREREREVHgYGBnGeipMWeLXocoSdhzsgKa4BVeTDpBNCbEMG3tbk/BrCkKKhNak+2CpKRI8igxFluFAZJa49C+jQ1ck+HUVQa8KTZE71QlJwTxGIKWQZBNzNBkwHffrlOVee0mS3OwaXYZiO7BsgZlVYZSHPOhImkiaDv7bBdWorgh1e99sppBHU7BoWjkiSQNHOtKwHIFiv4aQriKSdut6jBSds5+CPYw7adjwaPKIOiciIiIiIhqZ+NQxTPVWmPJgexJpy0Zr3EA0ZfW6QsYNomgQkpuNkDZtxAwbgH3M763KEryagiKfCl2R4Qj3QbUtbiBhMiukJ7LkdqARQuT9TGzbgRsuEUjbAl5JhirLsGwbsiQh5NVwpCONWROKMbks2GPr2hPHBFBdEcT2AxFUVwQR9nsQ9rtFVoUQqG+MYdaE4ry6JMNd3jl5gnnLaYQQOBRJjrhzIiIiIiKikYmBkWGor8KUU/QA/vOjJgghoCkSTFtCQAc8mgY1syzGFg5iaRttcbNf9Vezy2pCXg1+XYEkSUhnskJaDOOYdUbIrfXhZCJGWqaoiwDQljQy+9zXWbYNVXE7+wgB1DfGUBrQsWxOFT441NFr69rlcyagoS2Zq8vh091A2aFIMnf8SCpSKsvSqDsnIiIiIiIamRgYGYb6KkwZT9vQVQkSZGgKUBrQkbYcxNIW4kb/szncoqAKirwqFFmG7QgkTRvNMQMJwyq4DjKfFQluoCnLctwMHEVyAye2cOuM6IoMTZVxcmUIy+ZUAcAxW9def35NLnBypMMNnMyaUIxlc6pGZFvbGVXhUXdOREREREQ08jAwMgx1LkypSBJURYKmyJAh0B5PQwg3EyHRz0CILLmdPnyajJBHhVd3f+xp00ZHykLSsJkV8hmQAOiqlFd3RUKmlXIXYZ+KtZdMx5QKt+PKupc+OGbr2jUXfx7TL57ebanNSM6qmFEVxvRxRaPqnIiIiIiIaGRhYGQYCvtUjC3ywKvISNs29rQkcaA1icMdqX4FMLKPlLoqo9inIOz3wLYFLCGQNGw0RdPMChkAAoDjAF5VRsp0EPAqSJsOLEe4bXWlbCFbBaYjcDCSRM3YEHY3xY6rde1oa18ry9KoOyciIiIiIho5GBgZJtx2uQ72NMXxZn0zdhzqwP7WRL+6vsgSoEgSdFWCV5UR9uuQBOBIEmzbQdKw0JYwEU/b/ao5Qt2FdAnFAS+aYmmYlgMhAFl2a4cIAYQ8CmwAJT4NFSEd7x3oQMijoiygIGk6sB0BRXazdgSA5piBpmgaAFvXEhERERERDSUGRoZI2rSRtmwcaEti8+5WvLO3FVv3teNwR+qYx/o0GT5Nge0I2LYDj67CrytQFRkQAoZlQ8Bt5dsUM+AIkWslS59MyhaQJAkSJHg0JZe5I0EAEuD3aNBVGYblwBISZEmC5Qj4ZBkBj5z3XknDgqpIKA+5nWXYupaIiIiIiGjo8ElrkBmWgz99cASb/n4YW/e1YX9b8pjHeDUZAV1FsV/DpBI//B4FjR0pdKRMGDZgOQ4SaRsJw0DSdNvxBj0KThgTQEvcAMuHfHqWDdi2DSEEVEWGIiGXzaMpbiYIJAm24yBtWCjyqkikbQQ9DiTpaGBECAcdSRMnjAngzCllANi6loiIiIiIaCgxMDLIVv5qC7bsae3zNRNKfKidVIzaicWYOSGMlGEjbQlEUybe2tWC5lgaqUytkGja7nG5TSxtY29zDEY/luLQsemqjLak6XaWsR10LgUS8mqQJAkp0y1iWxby4ou1VXjsjb1o7EijyHc0m6QjacKnK7h64WSoqhswYetaIiIiIiKiocPAyCCybAfv7W/vtn1skRezJxajdlIxZk8sRnnIA0kCZEmCV1Pg02Toioyf/2UXDkaSaOxIobEjifZU36kgcYOpIp+V/3HJdPyvLf9AfVMcKcuBKksIelQEPAoACR1JE2nbwaQSP26+6HOYNaEYE0r8+OWru3AwkkJHyl0+c8KYAK5eOBmXzq7Ke3+2riUiIiIiIhoaDIwMIlWRse7SGfjdO/tRGtAxe0IYsycVY1zYByBTRFWW4dcVeDQZPk2FkskS2N0Uw7b97dAUGR0pG5bD7IHBclbNGFyx4AQsP3Uifv3/9uF37+yHYdmYWhFCwKOiKZbG4UgKpUEdt3zhc5g5oRgAcOnsKlw8Yxze2NWMpmga5SEPzpxSlssU6Yqta4mIiIiIiAYfAyOD7CunTcRXTpuIf7TE4TgCsiRBU91iqj7NDYh0bdkKHO1c4vWqbtFVwSUyx0uR3GUrnZceSQAUGbCdnovTnlUzBk9+cwEAQFVlfP2MEzHnhJJcZkdTzIBHk3H6lLIeMztUVcbCaRX9HiNb1xIREREREQ0uBkaGiF9X4dVkeDUFmtJzBkFn2c4llnDbvio9BE8KjU+TcXZ1GaJpE7uaEpg8xo+4aSNpWEiZDtoSBmwHCHgU1FSEsGrRFAS9Kj463IFdTXFIkoQzppTijBPLsOUfrdjbGsOm7UdgWDYmjQniR5dMRzCgd/u+zOwgIiIiIiIaPYZlYKS1tRVXXHEFbr/9dsyfPx8AsG3bNtx+++2or69HSUkJVq1ahcsvvzx3zHPPPYcHH3wQTU1NmDx5Mn7wgx+gtrYWgNtN5Kc//SleeOEFJJNJLFiwAD/60Y9QUdH/T/I/a9lWrf3VuXNJyKsgZRgDNLKR48QxATy4Yi5kWcK6lz7A9gMRnDw+3K2rS31jDNPHh3F2TTlkWcLcE0q7vdfCaRVYiAp84/TJ/frezOwgIiIiIiIaHY6dqjDI3n33XVxxxRXYt29fblskEsHVV1+NL33pS3j77bdxxx134Mc//jHef/99AMCWLVuwbt063HnnnXj77bfxxS9+EatWrUIy6bbCfeihh/DGG2/gmWeeweuvvw6v14s1a9YMyfl9UtnOJaUBHZoiw6tp6KVURY5XHb0ZDAFdwX9dNAWqKuddm/rGGGIpC7YjEEtZqG+MsasLERERERER9WpYBUaee+45fPe738UNN9yQt33Tpk0oLi7GihUroKoqTj/9dCxduhQbN24EADz11FO4+OKLMXfuXGiahn/+539GSUkJXn755dz+b33rWxg3bhyCwSBuvfVWvPbaa9i/f/+gn+Onke1cMn/yGIwt9kHvIzKiyEDY78G4sBcjNRzQ27grgjrWL5uZ19kle21mTgijPWlgb3Mc7UkDsyYU4/rza9jVhYiIiIiIiHo0rJbSnHXWWVi6dClUVc0LjtTV1WHq1Kl5r62ursbTTz8NAKivr8fy5cu77d+5cyei0SgOHz6cd3xZWRnC4TA++ugjTJw4cQDP6LOXrW/xel0T7vnTx247X0Vgb2sKhu0g6FExc3wIrUkHbYk0brhgKk6bWIL1mz7Epr8fgWFa0BQZhi0QN2zYjtsNpyKkY3JZEO/sa4MsyagIqmhLWoinbcgyYNvAQDX/9WkySv0qomkHpu2guiKIUr+KSNLGZadWoaE1iY8bYyjxa7hoxjgsnFrRY2cX1v4gIiIiIiKi4zWsAiPl5eU9bo/H4/D5fHnbvF4vEonEMffH43EAgN/v77Y/u2+kkWUJxX4dHlXBCWMCUGQJ40pDea8pVwXiaQvFfh1+v4bbvzQLXz0tkuumkjbdIETacuBRZWiKjLjpoNinY9bEYpQFPWiJpfG3fe3QFQlNsTQsR0CInru3AG6AxaspMC3HfW0/z0cC4NV1eDSBaMrC+LAPYb+OpBnHzKoSXLngpOO6Nqz9QURERERERP01rAIjvfH5fIhGo3nbUqkUAoFAbn8qleq2v6SkJBcwydYb6en4kSjbpSZp2Ah6u/8Yk4YNjyYj1GlfTxkVk0r82NeWQDRloT1h4JHXd8OrKgAATZGhyBIEkCloKiABkCQ3mJHteitLgJONgghAyttwbNnsD8txO+5oqtzj+ImIiIiIiIg+a8Oqxkhvpk6dirq6urxt9fX1qKmpAQDU1NT0uj8cDqOyshL19fW5fU1NTWhvb++2PGckyXapORRJQoj8IIQQAociSdRUhHDimPzgTzaj4pSJxZhcHoSqyrmvz64pR01lKPeeIa+KIp8Kw3agyYAQblBElvKzRoQ4Wg/Esm14VRmy3HuNkK5KvAoAgYRhIezTENSVXsdPRERERERE9FkaEYGRxYsXo7m5GY899hhM08TmzZvx4osv5uqKXHbZZXjxxRexefNmmKaJxx57DC0tLVi8eDEAYNmyZXjooYewf/9+xGIxrF+/HvPmzcOkSZOG8rQ+lYHoxNL1PeNpO7NUR4Yt3IKubuaIhC6xGCiylAmYuBkmRR4VqnLs7+1VZUTTDlrjBjyKjMoiD+qb4uwkQ0RERERERINiRKxTKCkpwYYNG3DHHXfg/vvvR2lpKdasWYMFCxYAAE4//XSsXbsWP/zhD3HkyBFUV1fjkUceQXFxMQBg9erVsCwLK1asQDwex/z58/Gzn/1s6E7oM5LtxJKtG3Kkw4FHkzFrQjGWzan6RJ1Yur5n2nQwPuzFmIAOWzhoiqYRS1vI5oNkM0g0VYEuS7mlMCV+HZbjvj6SMHPLbjorC2ioLPKhLWkAAIr9OgTwqcZPREREREREdDwk0XUdBnUTi8Uwd+5cvPvuuwgGh19hT8cRn3knlq7vma1FEkmaaE8YiBs2ACDoURDyaoilLBT5tFxNkHjaRsirYkLYh7f2tGBfWxyvf9QM23Fwwpgg/vv5NWhMGIimLAQ8St4x7CRDRHRsw31uGol4TYmIaDjhvDR4RkTGCPVtIDqx9PSen/R7LJxWAQBY2aW7TDCgf7LBEREREREREX1GRkSNESIiIiIiIiKigcDACBEREREREdEoYds2Vq5ciVtuuSW3bdu2bbj88stRW1uL8847D0899dQQjnD4YWCEiIiIiIiIaJT4+c9/jnfeeSf3dSQSwdVXX40vfelLePvtt3HHHXfgxz/+Md5///0hHOXwwsAIERERERER0Sjw1ltvYdOmTViyZElu26ZNm1BcXIwVK1ZAVVWcfvrpWLp0KTZu3DiEIx1eGBghIiIiIiIiGuFaWlpw66234t/+7d/g8/ly2+vq6jB16tS811ZXV2Pnzp2DPcRhi11p+iHb0TgWiw3xSIiIiFzZOSk7R9Gnx/meiIiGk+x8FI1G87brug5dz+/w6TgObrzxRlx11VX43Oc+l7cvHo/nBUoAwOv1IpFIDMCoRyYGRvohHo8DABYuXDjEIyEiIsoXj8cRCoWGehijAud7IiIajhYtWpT39bXXXovrrrsub9svf/lL6LqOlStXdjve5/N1C66kUikEAoHPfKwjFQMj/VBRUYFXX30VgUAAkiQN9XCIiIgghEA8HkdFRcVQD2XU4HxPRETDiRAC7e3tCIfDkOWjVTC6ZosAwAsvvIDGxkaceuqpANzABwC88soruOmmm/DGG2/kvb6+vh41NTUDOPqRRRLMwSUiIiIiIiIaNbKteu+88060tbVhyZIlWL16NVasWIF3330X11xzDR588EEsWLBgiEc6PLD4KhEREREREdEoVVJSgg0bNuCPf/wj5s+fjzVr1mDNmjUMinTCjBEiIiIiIiIiKljMGCEiIiIiIiKigsXACBEREREREREVLAZGiIiIiIiIiKhgMTBCRERERERERAWLgREiIiIiIiIiKlgMjAywl19+GdOnT0dtbW3uz4033ggA2LZtGy6//HLU1tbivPPOw1NPPZV37HPPPYfFixdj9uzZWLZsGf72t7/l9tm2jZ/85Cc444wzUFtbi1WrVqGxsXFQz+14tba2YvHixdiyZUtu20Beg5aWFlxzzTU49dRTMX/+fNxxxx2wLGvgT7Sferoea9euxYwZM/Lul9/97ne5/aPxeuzcuRNXXXUV5s2bhzPPPBM33XQTWltbARTm/dHX9SjE+wMA3nrrLVx++eWYM2cOzjzzTKxbtw6pVApAYd4jNDxxvndxrs/Hud7Fub47zvf5ONfTkBM0oO68805xyy23dNve3t4u5s2bJ5588klhmqZ48803RW1trdi2bZsQQojNmzeL2tpa8c477wjDMMSjjz4q5s+fLxKJhBBCiAceeEAsXbpUHDx4UESjUfHtb39bfOtb3xrUczse77zzjrjgggvE1KlTxebNm4UQA38NrrzySvGd73xHJBIJsW/fPnHxxReLRx55ZPBPvgc9XQ8hhPjyl78snn322R6PGY3XI5lMijPPPFPcd999Ip1Oi9bWVvGtb31L/Ou//mtB3h99XQ8hCu/+EEKIlpYWMXPmTPHMM88I27bFkSNHxCWXXCLuu+++grxHaPjifM+5vivO9S7O9d1xvs/HuZ6GAwZGBtiKFSvEk08+2W3773//e7FkyZK8bbfddpu46aabhBBCfOc73xFr1qzJ2/+FL3xBPP3000IIIc455xzxhz/8IbevqalJTJs2Tezbt++zPoVP7dlnnxWLFi0SL730Ut4vBwN5Dfbu3SumTp0qDh8+nNv/0ksviUWLFg3IOR6P3q5HOp0WJ598svj44497PG40Xo9du3aJb37zm8KyrNy2V155RcyZM6cg74++rkch3h9Z0WhUCCGE4zjio48+EosXLxZPPPFEQd4jNHwV+nzPuT4f5/qjONd3x/m+O871NNS4lGYAOY6Dv//97/jLX/6Cc889F+eccw5+8IMfIBKJoK6uDlOnTs17fXV1NXbu3AkAqK+v73V/NBrF4cOH8/aXlZUhHA7jo48+GvgTO05nnXUW/vSnP+Gf/umf8rYP5DWoq6tDcXExKisrc/unTJmCgwcPoqOj47M+xePS2/XYuXMnLMvC/fffjzPOOAMXXnghHn74YTiOA2B0Xo/Jkyfj3//936EoSm7bf/zHf+Dkk08uyPujr+tRiPdHVjAYBAAsXLgQS5cuRXl5OZYtW1aQ9wgNT5zvOdd3xbn+KM713XG+745zPQ01BkYGUGtrK6ZPn44LL7wQL7/8Mn77299i7969uPHGGxGPx+Hz+fJe7/V6kUgkAKDP/fF4HADg9/u77c/uG07Ky8uhqmq37QN5DXo6Nvt19v2HSm/XIxqNYt68eVi5ciVeffVV3H333XjiiSewYcMGAKP3emQJIXDvvffiP//zP3HrrbcW7P2R1fV6FPr9AQCbNm3Ca6+9BlmWcf311xf8PULDB+d7zvVdca7vGef67jjf5+NcT0OFgZEBVFZWho0bN+Kyyy6Dz+fD+PHjceONN+K1116DECJXUCgrlUohEAgAcP9R9rY/+w82mUz2evxI0Nc5Hmv/sa6B3+/vti/79XC9RmeeeSYef/xxzJs3D5qmYdasWfjGN76Bl19+GcDovh6xWAzXX389XnzxRTz55JOYNm1aQd8fPV2PQr4/srxeLyorK3HjjTfi9ddfL+h7hIYXzve947/TfIX8fznn+u4433fHuZ6GCgMjA2jnzp346U9/CiFEbpthGJBlGbNmzUJdXV3e6+vr61FTUwMAqKmp6XV/OBxGZWUl6uvrc/uamprQ3t7eLZVsOJs6deqAXYOamhq0t7ejubk5t3/Xrl0YO3YsQqHQAJ7VJ/fKK6/gt7/9bd42wzDg9XoBjN7rsW/fPixfvhyxWAxPP/00pk2bBqBw74/erkeh3h9bt27FF77wBRiGkdtmGAY0TUN1dXVB3iM0/HC+712h/l/em0L9v5xzfXec74/iXE/DwtCVNxn9Dh06JGbPni0efvhhYZqmaGhoEF/5ylfE97//fdHa2ipOPfVU8eijjwrDMMRbb70lamtrxVtvvSWEELmKy2+99VauwvJpp50m2trahBBC3HvvveKSSy4R+/bty1VYvvLKK4fwbPuncwGygb4GX/va18QNN9wgotForsr0/fffP+jn3JfO12PTpk1i1qxZ4s033xSO44itW7eK+fPni+eff14IMTqvR3t7u1i0aJG45ZZbhG3befsK8f7o63oU4v0hhBCxWEwsXLhQrF+/XqTTaXHgwAFx2WWXibVr1xbkPULDE+f7fJzr83Gu51zfFef7fJzraThgYGSAbdmyRVxxxRWitrZWLFiwQKxbt06kUikhhBDvv/9+bt/5558vnnnmmbxjn3/+eXHhhReK2bNni8suu0y89957uX2GYYi7775bnH322WLOnDli1apVorm5eVDP7ZPo2rJuIK9BU1OTuO6668S8efPEggULxJ133plX/Xs46Ho9fvOb34glS5aIU045RZx//vndOhyMtuuxYcMGMXXqVHHKKaeI2bNn5/0RovDuj2Ndj0K7P7Lq6urEVVddJU499VRx7rnninvuuUek02khROHdIzR8cb4/inN9Ps71nOu74nzfHed6GmqSEJ3yPomIiIiIiIiICghrjBARERERERFRwWJghIiIiIiIiIgKFgMjRERERERERFSwGBghIiIiIiIiooLFwAgRERERERERFSwGRoiIiIiIiIioYDEwQkREREREREQFi4ERIurTAw88gGnTpnX7M2PGDJx11llYvXo1tm7desz32bJlC6ZNm4YHHnhgEEZNRERE/cW5nogKnTrUAyCikeGKK67A3Llzc19bloWDBw/i17/+Nf7yl7/gF7/4Bc4+++xej58yZQruuusuTJs2bTCGS0RERMeJcz0RFSoGRoioX2bPno1LL7202/bzzjsPy5cvx1133dXnL0tlZWU9Hk9ERETDA+d6IipUXEpDRJ/KySefjJqaGnz88ceIRCJDPRwiIiL6jHGuJ6LRjoERIvrUZNn9r8S2baxcuRKXXHIJnn76aZxxxhmYPXs27rnnnl7XHW/fvh3XXXcdTj/9dNTW1uLSSy/F7373Owgh8l73+uuv4+tf/zrmzJmDU045BcuWLcOzzz47aOdIRERUyDjXE9FoxqU0RPSpHDp0CLt370ZVVRVKS0sBAAcOHMBPfvITXH311XAcB3PnzoVt292Off3117Fq1SoEg0F87WtfQ0VFBf70pz/htttuw8GDB3HDDTcAADZu3Ih169Zh5syZuPbaayHLMv785z/je9/7Hj788EPceuutg3rOREREhYRzPRGNdgyMEFG/JBIJtLa25r42TRM7d+7EfffdB8MwsHr16ty+ZDKJG2+8EStWrMht27JlS977CSGwdu1aBAIBvPDCC6isrATgFn678sorsWHDBlx11VVIpVL48Y9/jEWLFuGhhx6CJEkAgG984xu4+eab8fjjj2Pp0qWYNWvWQJ4+ERHRqMe5nogKFQMjRNQv69atw7p167ptLy8vx2233Ybly5fnbT///PP7fL8PPvgADQ0N+PrXv577RQlwU3XvvvtupNNpBINB/OEPf4BpmrjooovQ1taW9x4XX3wxXnjhBWzatIm/LBEREX1KnOuJqFAxMEJE/fLNb34TZ511Vu5rXddRXl6OSZMm5T7Z6aysrKzP9ztw4AAAt7VfV1VVVbm/79mzBwBw00039fpeDQ0NfQ+eiIiIjolzPREVKgZGiKhfqqurccYZZ/T79ara938vlmUBQI+/aHWWXa+8bt06TJgwocfXZNc7ExER0SfHuZ6IChUDI0Q0JLK/+GQ/JersjTfewPPPP49/+Zd/yb2uqKio2y9rjY2NeP/99zFx4sSBHzAREREdF871RDRSsF0vEQ2Jk08+GWPHjsWLL76YV+hNCIFf/epXeOmll1BeXo4lS5ZAlmX84he/QCqVynuPO++8E6tXr8aOHTsGe/hERER0DJzriWikYMYIEQ0JVVXxox/9CKtXr8all16Kr371qygpKcErr7yCN954A9/97ndRWlqK0tJSXHfddbjvvvtw6aWX4stf/jKKiorw5z//GX/9619x7rnnYsmSJUN9OkRERNQF53oiGikYGCGiIbNo0SJs3LgRDz74IB599FHYto0pU6bgnnvuwcUXX5x73TXXXIPq6mo8/vjjePjhh+E4DiZOnIibbroJK1euhKIoQ3gWRERE1BvO9UQ0EkhCCDHUgyAiIiIiIiIiGgqsMUJEREREREREBYuBESIiIiIiIiIqWAyMEBEREREREVHBYmCEiIiIiIiIiAoWAyNEREREREREVLAYGCEiIiIiIiKigsXACBEREREREREVLAZGiIiIiIiIiKhgMTBCRERERERERAWLgREiIiIiIiIiKlgMjBARERERERFRwWJghIiIiIiIiIgKFgMjRERERERERFSw/j9X6ARAvXOJswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "\n",
    "sns.regplot(x='Price',\n",
    "            y='Age',\n",
    "            data=dataset,\n",
    "            scatter_kws={'alpha': 0.6},\n",
    "            ax=axes[0, 0])\n",
    "axes[0, 0].set_xlabel('Price', fontsize=14)\n",
    "axes[0, 0].set_ylabel('Age', fontsize=14)\n",
    "axes[0, 0].yaxis.tick_left()\n",
    "\n",
    "sns.regplot(x='Price',\n",
    "            y='KM',\n",
    "            data=dataset,\n",
    "            scatter_kws={'alpha': 0.6},\n",
    "            ax=axes[0, 1])\n",
    "axes[0, 1].set_xlabel('Price', fontsize=14)\n",
    "axes[0, 1].set_ylabel('KM', fontsize=14)\n",
    "axes[0, 1].yaxis.set_label_position(\"right\")\n",
    "axes[0, 1].yaxis.tick_right()\n",
    "\n",
    "sns.regplot(x='Price',\n",
    "            y='Weight',\n",
    "            data=dataset,\n",
    "            scatter_kws={'alpha': 0.6},\n",
    "            ax=axes[1, 0])\n",
    "axes[1, 0].set_xlabel('Price', fontsize=14)\n",
    "axes[1, 0].set_ylabel('Weight', fontsize=14)\n",
    "\n",
    "sns.regplot(x='Price',\n",
    "            y='HP',\n",
    "            data=dataset,\n",
    "            scatter_kws={'alpha': 0.6},\n",
    "            ax=axes[1, 1])\n",
    "axes[1, 1].set_xlabel('Price', fontsize=14)\n",
    "axes[1, 1].set_ylabel('HP', fontsize=14)\n",
    "axes[1, 1].yaxis.set_label_position(\"right\")\n",
    "axes[1, 1].yaxis.tick_right()\n",
    "axes[1, 1].set(ylim=(40, 160))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\student\\AppData\\Local\\Temp\\ipykernel_9964\\3333426495.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset['KM'], ax = axes[0])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1,2,figsize=(14,4))\n",
    "\n",
    "sns.distplot(dataset['KM'], ax = axes[0])\n",
    "axes[0].set_xlabel('KM', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.scatterplot(x = 'Price', y = 'KM', data = dataset, ax = axes[1])\n",
    "axes[1].set_xlabel('Price', fontsize=14)\n",
    "axes[1].set_ylabel('KM', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Petrol': 1264, 'Diesel': 155, 'CNG': 17})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_list= Counter(dataset['FuelType'])\n",
    "fuel_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Diesel\n",
       "1       Diesel\n",
       "2       Diesel\n",
       "3       Diesel\n",
       "4       Diesel\n",
       "         ...  \n",
       "1431    Petrol\n",
       "1432    Petrol\n",
       "1433    Petrol\n",
       "1434    Petrol\n",
       "1435    Petrol\n",
       "Name: FuelType, Length: 1436, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['FuelType']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Diesel', 'Petrol', 'CNG']) dict_values([155, 1264, 17])\n"
     ]
    }
   ],
   "source": [
    "labels = fuel_list.keys()\n",
    "sizes = fuel_list.values()\n",
    "print(labels,sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Diesel'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 7\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#fuel_list= Counter(dataset['FuelType'])\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#labels = fuel_list.keys()\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#sizes = fuel_list.values()\u001b[39;00m\n\u001b[0;32m      5\u001b[0m f, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m,\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m----> 7\u001b[0m sns\u001b[38;5;241m.\u001b[39mcountplot(dataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFuelType\u001b[39m\u001b[38;5;124m'\u001b[39m], ax \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;241m0\u001b[39m], palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSet1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      8\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFuel Type\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n\u001b[0;32m      9\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2943\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2940\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2941\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass values for both `x` and `y`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 2943\u001b[0m plotter \u001b[38;5;241m=\u001b[39m _CountPlotter(\n\u001b[0;32m   2944\u001b[0m     x, y, hue, data, order, hue_order,\n\u001b[0;32m   2945\u001b[0m     estimator, errorbar, n_boot, units, seed,\n\u001b[0;32m   2946\u001b[0m     orient, color, palette, saturation,\n\u001b[0;32m   2947\u001b[0m     width, errcolor, errwidth, capsize, dodge\n\u001b[0;32m   2948\u001b[0m )\n\u001b[0;32m   2950\u001b[0m plotter\u001b[38;5;241m.\u001b[39mvalue_label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1530\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[0;32m   1526\u001b[0m              estimator, errorbar, n_boot, units, seed,\n\u001b[0;32m   1527\u001b[0m              orient, color, palette, saturation, width,\n\u001b[0;32m   1528\u001b[0m              errcolor, errwidth, capsize, dodge):\n\u001b[0;32m   1529\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1530\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_variables(x, y, hue, data, orient,\n\u001b[0;32m   1531\u001b[0m                              order, hue_order, units)\n\u001b[0;32m   1532\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[0;32m   1533\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, errorbar, n_boot, seed)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:516\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    513\u001b[0m     plot_data \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m    515\u001b[0m \u001b[38;5;66;03m# Convert to a list of arrays, the common representation\u001b[39;00m\n\u001b[1;32m--> 516\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39masarray(d, \u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m plot_data]\n\u001b[0;32m    518\u001b[0m \u001b[38;5;66;03m# The group names will just be numeric indices\u001b[39;00m\n\u001b[0;32m    519\u001b[0m group_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(plot_data)))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:516\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    513\u001b[0m     plot_data \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m    515\u001b[0m \u001b[38;5;66;03m# Convert to a list of arrays, the common representation\u001b[39;00m\n\u001b[1;32m--> 516\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39masarray(d, \u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m plot_data]\n\u001b[0;32m    518\u001b[0m \u001b[38;5;66;03m# The group names will just be numeric indices\u001b[39;00m\n\u001b[0;32m    519\u001b[0m group_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(plot_data)))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:917\u001b[0m, in \u001b[0;36mSeries.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m    870\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    871\u001b[0m \u001b[38;5;124;03mReturn the values as a NumPy array.\u001b[39;00m\n\u001b[0;32m    872\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[38;5;124;03m      dtype='datetime64[ns]')\u001b[39;00m\n\u001b[0;32m    915\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    916\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[1;32m--> 917\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(values, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_copy_on_write() \u001b[38;5;129;01mand\u001b[39;00m astype_is_view(values\u001b[38;5;241m.\u001b[39mdtype, arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[0;32m    919\u001b[0m     arr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mview()\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Diesel'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fuel_list= Counter(dataset['FuelType'])\n",
    "#labels = fuel_list.keys()\n",
    "#sizes = fuel_list.values()\n",
    "\n",
    "f, axes = plt.subplots(1,2,figsize=(14,4))\n",
    "\n",
    "sns.countplot(dataset['FuelType'], ax = axes[0], palette=\"Set1\")\n",
    "axes[0].set_xlabel('Fuel Type', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.violinplot(x = 'FuelType', y = 'Price', data = dataset, ax = axes[1])\n",
    "axes[1].set_xlabel('Fuel Type', fontsize=14)\n",
    "axes[1].set_ylabel('Price', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\student\\AppData\\Local\\Temp\\ipykernel_9964\\1856679940.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset['HP'], ax = axes[0])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1,2,figsize=(14,4))\n",
    "\n",
    "sns.distplot(dataset['HP'], ax = axes[0])\n",
    "axes[0].set_xlabel('HP', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.scatterplot(x = 'HP', y = 'Price', data = dataset, ax = axes[1])\n",
    "axes[1].set_xlabel('HP', fontsize=14)\n",
    "axes[1].set_ylabel('Price', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python36\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "d:\\python36\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1377: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "d:\\python36\\lib\\site-packages\\matplotlib\\axes\\_base.py:237: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "d:\\python36\\lib\\site-packages\\matplotlib\\axes\\_base.py:239: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "sns.distplot(dataset['MetColor'], ax=axes[0])\n",
    "axes[0].set_xlabel('MetColor', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.boxplot(x='MetColor', y='Price', data=dataset, ax=axes[1])\n",
    "axes[1].set_xlabel('MetColor', fontsize=14)\n",
    "axes[1].set_ylabel('Price', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\student\\AppData\\Local\\Temp\\ipykernel_9964\\4115823405.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset['Automatic'], ax=axes[0])\n"
     ]
    },
    {
     "data": {
      "image/png": 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AALdHSAYAAAAAAAC3R0gGAAAAAAAAt0dIBgAAAAAAALf3/wFYCtGHAbCYPAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "sns.distplot(dataset['Automatic'], ax=axes[0])\n",
    "axes[0].set_xlabel('Automatic', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.boxenplot(x='Automatic', y='Price', data=dataset, ax=axes[1])\n",
    "axes[1].set_xlabel('Automatic', fontsize=14)\n",
    "axes[1].set_ylabel('Price', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python36\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "d:\\python36\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1377: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "d:\\python36\\lib\\site-packages\\matplotlib\\axes\\_base.py:237: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "d:\\python36\\lib\\site-packages\\matplotlib\\axes\\_base.py:239: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "sns.distplot(dataset['CC'], ax=axes[0])\n",
    "axes[0].set_xlabel('CC', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.boxplot(x='CC', y='Price', data=dataset, ax=axes[1])\n",
    "axes[1].set_xlabel('CC', fontsize=14)\n",
    "axes[1].set_ylabel('Price', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\student\\AppData\\Local\\Temp\\ipykernel_9964\\2516310998.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset['Doors'], ax=axes[0])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "sns.distplot(dataset['Doors'], ax=axes[0])\n",
    "axes[0].set_xlabel('Doors', fontsize=14)\n",
    "axes[0].set_ylabel('Count', fontsize=14)\n",
    "axes[0].yaxis.tick_left()\n",
    "\n",
    "sns.boxenplot(x='Doors', y='Price', data=dataset, ax=axes[1])\n",
    "axes[1].set_xlabel('Doors', fontsize=14)\n",
    "axes[1].set_ylabel('Price', fontsize=14)\n",
    "axes[1].yaxis.set_label_position(\"right\")\n",
    "axes[1].yaxis.tick_right()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1436, 12)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Age</th>\n",
       "      <th>KM</th>\n",
       "      <th>HP</th>\n",
       "      <th>MetColor</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>CC</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FuelType_CNG</th>\n",
       "      <th>FuelType_Diesel</th>\n",
       "      <th>FuelType_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13500</td>\n",
       "      <td>23</td>\n",
       "      <td>46986</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13750</td>\n",
       "      <td>23</td>\n",
       "      <td>72937</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13950</td>\n",
       "      <td>24</td>\n",
       "      <td>41711</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14950</td>\n",
       "      <td>26</td>\n",
       "      <td>48000</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1165</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13750</td>\n",
       "      <td>30</td>\n",
       "      <td>38500</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>1170</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Price  Age     KM  HP  MetColor  Automatic    CC  Doors  Weight  \\\n",
       "0  13500   23  46986  90         1          0  2000      3    1165   \n",
       "1  13750   23  72937  90         1          0  2000      3    1165   \n",
       "2  13950   24  41711  90         1          0  2000      3    1165   \n",
       "3  14950   26  48000  90         0          0  2000      3    1165   \n",
       "4  13750   30  38500  90         0          0  2000      3    1170   \n",
       "\n",
       "   FuelType_CNG  FuelType_Diesel  FuelType_Petrol  \n",
       "0         False             True            False  \n",
       "1         False             True            False  \n",
       "2         False             True            False  \n",
       "3         False             True            False  \n",
       "4         False             True            False  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop('Price', axis = 1).values\n",
    "y = dataset.iloc[:, 0].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13500],\n",
       "       [13750],\n",
       "       [13950],\n",
       "       ...,\n",
       "       [ 8500],\n",
       "       [ 7250],\n",
       "       [ 6950]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 46986, 90, ..., False, True, False],\n",
       "       [23, 72937, 90, ..., False, True, False],\n",
       "       [24, 41711, 90, ..., False, True, False],\n",
       "       ...,\n",
       "       [71, 17016, 86, ..., False, False, True],\n",
       "       [70, 16916, 86, ..., False, False, True],\n",
       "       [76, 1, 110, ..., False, False, True]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       13500\n",
       "1       13750\n",
       "2       13950\n",
       "3       14950\n",
       "4       13750\n",
       "        ...  \n",
       "1431     7500\n",
       "1432    10845\n",
       "1433     8500\n",
       "1434     7250\n",
       "1435     6950\n",
       "Name: Price, Length: 1436, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (1077, 11)\n",
      "Shape of X_test:  (359, 11)\n",
      "Shape of y_train:  (1077, 1)\n",
      "Shape of y_test (359, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train: \",X_train.shape)\n",
    "print(\"Shape of X_test: \", X_test.shape)\n",
    "print(\"Shape of y_train: \",y_train.shape)\n",
    "print(\"Shape of y_test\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  4.回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor_linear = LinearRegression()\n",
    "regressor_linear.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  0.8480754345159047\n",
      "R2_score (train):  0.8702260786694703\n",
      "R2_score (test):  0.8621869690956067\n",
      "RMSE:  1398.4596051422195\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Predicting Cross Validation Score the Test set results\n",
    "cv_linear = cross_val_score(estimator=regressor_linear,\n",
    "                            X=X_train,\n",
    "                            y=y_train,\n",
    "                            cv=10)\n",
    "\n",
    "# Predicting R2 Score the Train set results\n",
    "y_pred_linear_train = regressor_linear.predict(X_train)\n",
    "r2_score_linear_train = r2_score(y_train, y_pred_linear_train)\n",
    "\n",
    "# Predicting R2 Score the Test set results\n",
    "y_pred_linear_test = regressor_linear.predict(X_test)\n",
    "r2_score_linear_test = r2_score(y_test, y_pred_linear_test)\n",
    "\n",
    "# Predicting RMSE the Test set results\n",
    "rmse_linear = (np.sqrt(mean_squared_error(y_test, y_pred_linear_test)))\n",
    "print(\"CV: \", cv_linear.mean())\n",
    "print('R2_score (train): ', r2_score_linear_train)\n",
    "print('R2_score (test): ', r2_score_linear_test)\n",
    "print(\"RMSE: \", rmse_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.21164453e+02, -1.48789981e-02,  4.55824003e+01,\n",
       "         1.04801739e+02,  2.36006714e+02, -3.12702199e+00,\n",
       "        -3.16952768e+01,  2.31842274e+01, -1.21631013e+03,\n",
       "         1.08295324e+03,  1.33356891e+02]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_linear.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6201.92777117])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_linear.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二阶多项式回归\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree = 2)\n",
    "X_poly = poly_reg.fit_transform(X_train)\n",
    "poly_reg.fit(X_poly, y_train)\n",
    "regressor_poly2 = LinearRegression()\n",
    "regressor_poly2.fit(X_poly, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  0.8480754345159047\n",
      "R2_score (train):  0.9157086185582459\n",
      "R2_score (test):  0.7619825548160413\n",
      "RMSE:  1837.846259439408\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Predicting Cross Validation Score the Test set results\n",
    "cv_poly2 = cross_val_score(estimator = regressor_poly2, X = X_train, y = y_train, cv = 10)\n",
    "\n",
    "# Predicting R2 Score the Train set results\n",
    "y_pred_poly2_train = regressor_poly2.predict(poly_reg.fit_transform(X_train))\n",
    "r2_score_poly2_train = r2_score(y_train, y_pred_poly2_train)\n",
    "\n",
    "# Predicting R2 Score the Test set results\n",
    "y_pred_poly2_test = regressor_poly2.predict(poly_reg.fit_transform(X_test))\n",
    "r2_score_poly2_test = r2_score(y_test, y_pred_poly2_test)\n",
    "\n",
    "# Predicting RMSE the Test set results\n",
    "rmse_poly2 = (np.sqrt(mean_squared_error(y_test, y_pred_poly2_test)))\n",
    "print('CV: ', cv_poly2.mean())\n",
    "print('R2_score (train): ', r2_score_poly2_train)\n",
    "print('R2_score (test): ', r2_score_poly2_test)\n",
    "print(\"RMSE: \", rmse_poly2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.81510778e-01,  5.60270980e+02, -1.59382564e-01,\n",
       "        -4.25393396e+03, -4.41251396e+02,  1.93584439e+02,\n",
       "         3.69713987e+02, -2.88811142e+03, -6.70315621e+01,\n",
       "        -9.52376510e+01, -1.99579247e+04,  2.00531623e+04,\n",
       "        -1.03389103e-01,  4.02643444e-04, -6.15900330e-01,\n",
       "        -7.70781347e+00,  1.00772942e+01, -3.27565600e-03,\n",
       "         1.17751205e+01, -7.73593940e-01,  1.91966777e+02,\n",
       "         2.02177598e+02,  1.66089535e+02, -7.05583716e-08,\n",
       "         6.31025853e-05,  6.58235101e-03, -2.52022880e-04,\n",
       "        -2.55168388e-05, -3.43264277e-03,  2.02248384e-04,\n",
       "        -8.47868828e-02, -3.48800738e-02, -3.98387354e-02,\n",
       "        -1.54789382e+00,  4.73794834e+01, -1.81021952e+02,\n",
       "         7.93319442e-01, -1.53224155e+01,  8.67567518e-02,\n",
       "        -1.04761400e+04,  2.93737873e+03,  3.28482743e+03,\n",
       "        -4.41251304e+02, -2.30434885e+02, -4.32492857e+00,\n",
       "        -8.14384298e+01,  3.23108348e+00, -2.49536075e+03,\n",
       "         2.56940873e+03, -5.15299285e+02,  1.93584510e+02,\n",
       "         1.17958794e+01, -3.08619003e+02, -8.38863963e-01,\n",
       "        -1.68390602e+03,  1.45519152e-11,  1.87749053e+03,\n",
       "        -7.39998444e-02, -4.52561339e-01,  3.01340042e-02,\n",
       "         7.81445130e+02, -1.62158716e+02, -2.49572302e+02,\n",
       "        -2.43636377e+02,  7.12198784e+00, -6.47318302e+02,\n",
       "        -1.34749268e+03, -8.93300453e+02,  8.33870250e-03,\n",
       "        -6.44889373e+01, -2.25076500e+01,  1.99650188e+01,\n",
       "        -9.52376368e+01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.99579248e+04,  0.00000000e+00,  2.00531624e+04]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_poly2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-60768.63756059])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor_poly2.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scalar&#x27;, StandardScaler()),\n",
       "                (&#x27;poly&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;model&#x27;, Ridge(alpha=1777))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scalar&#x27;, StandardScaler()),\n",
       "                (&#x27;poly&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;model&#x27;, Ridge(alpha=1777))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=1777)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('poly', PolynomialFeatures(degree=3)),\n",
       "                ('model', Ridge(alpha=1777))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "steps = [\n",
    "    ('scalar', StandardScaler()),\n",
    "    ('poly', PolynomialFeatures(degree=3)),\n",
    "    ('model', Ridge(alpha=1777, fit_intercept=True))\n",
    "]\n",
    "\n",
    "ridge_pipe = Pipeline(steps)\n",
    "ridge_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  0.7785178588873507\n",
      "R2_score (train):  0.8700098556004301\n",
      "R2_score (test):  0.8697806448706523\n",
      "RMSE:  1359.3852529159876\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Predicting Cross Validation Score the Test set results\n",
    "cv_ridge = cross_val_score(estimator = ridge_pipe, X = X_train, y = y_train.ravel(), cv = 10)\n",
    "\n",
    "# Predicting R2 Score the Test set results\n",
    "y_pred_ridge_train = ridge_pipe.predict(X_train)\n",
    "r2_score_ridge_train = r2_score(y_train, y_pred_ridge_train)\n",
    "\n",
    "# Predicting R2 Score the Test set results\n",
    "y_pred_ridge_test = ridge_pipe.predict(X_test)\n",
    "r2_score_ridge_test = r2_score(y_test, y_pred_ridge_test)\n",
    "\n",
    "# Predicting RMSE the Test set results\n",
    "rmse_ridge = (np.sqrt(mean_squared_error(y_test, y_pred_ridge_test)))\n",
    "print('CV: ', cv_ridge.mean())\n",
    "print('R2_score (train): ', r2_score_ridge_train)\n",
    "print('R2_score (test): ', r2_score_ridge_test)\n",
    "print(\"RMSE: \", rmse_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 套索回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scalar&#x27;, StandardScaler()),\n",
       "                (&#x27;poly&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;model&#x27;, Lasso(alpha=2.36, max_iter=2000, tol=0.0199))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scalar&#x27;, StandardScaler()),\n",
       "                (&#x27;poly&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;model&#x27;, Lasso(alpha=2.36, max_iter=2000, tol=0.0199))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=2.36, max_iter=2000, tol=0.0199)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scalar', StandardScaler()),\n",
       "                ('poly', PolynomialFeatures(degree=3)),\n",
       "                ('model', Lasso(alpha=2.36, max_iter=2000, tol=0.0199))])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "steps = [('scalar', StandardScaler()), ('poly', PolynomialFeatures(degree=3)),\n",
    "         ('model',\n",
    "          Lasso(alpha=2.36, fit_intercept=True, tol=0.0199, max_iter=2000))]\n",
    "\n",
    "lasso_pipe = Pipeline(steps)\n",
    "lasso_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8936.88867116])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_pipe.predict(X_test[1].reshape(1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  0.742771262010789\n",
      "R2_score (train):  0.9273633923675705\n",
      "R2_score (test):  0.9022945020939637\n",
      "RMSE:  1177.5091354603408\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Predicting Cross Validation Score\n",
    "cv_lasso = cross_val_score(estimator = lasso_pipe, X = X_train, y = y_train, cv = 10)\n",
    "\n",
    "# Predicting R2 Score the Test set results\n",
    "y_pred_lasso_train = lasso_pipe.predict(X_train)\n",
    "r2_score_lasso_train = r2_score(y_train, y_pred_lasso_train)\n",
    "\n",
    "# Predicting R2 Score the Test set results\n",
    "y_pred_lasso_test = lasso_pipe.predict(X_test)\n",
    "r2_score_lasso_test = r2_score(y_test, y_pred_lasso_test)\n",
    "\n",
    "# Predicting RMSE the Test set results\n",
    "rmse_lasso = (np.sqrt(mean_squared_error(y_test, y_pred_lasso_test)))\n",
    "print('CV: ', cv_lasso.mean())\n",
    "print('R2_score (train): ', r2_score_lasso_train)\n",
    "print('R2_score (test): ', r2_score_lasso_test)\n",
    "print(\"RMSE: \", rmse_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.衡量误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    ('Linear Regression', rmse_linear, r2_score_linear_train,\n",
    "     r2_score_linear_test, cv_linear.mean()),\n",
    "    ('Polynomial Regression (2nd)', rmse_poly2, r2_score_poly2_train,\n",
    "     r2_score_poly2_test, cv_poly2.mean()),\n",
    "    ('Ridge Regression', rmse_ridge, r2_score_ridge_train, r2_score_ridge_test,\n",
    "     cv_ridge.mean()),\n",
    "    ('Lasso Regression', rmse_lasso, r2_score_lasso_train, r2_score_lasso_test,\n",
    "     cv_lasso.mean()),\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2_Score(training)</th>\n",
       "      <th>R2_Score(test)</th>\n",
       "      <th>Cross-Validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>1398.459605</td>\n",
       "      <td>0.870226</td>\n",
       "      <td>0.862187</td>\n",
       "      <td>0.848075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Polynomial Regression (2nd)</td>\n",
       "      <td>1837.846259</td>\n",
       "      <td>0.915709</td>\n",
       "      <td>0.761983</td>\n",
       "      <td>0.848075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>1359.385253</td>\n",
       "      <td>0.870010</td>\n",
       "      <td>0.869781</td>\n",
       "      <td>0.778518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>1177.509135</td>\n",
       "      <td>0.927363</td>\n",
       "      <td>0.902295</td>\n",
       "      <td>0.742771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model         RMSE  R2_Score(training)  \\\n",
       "0            Linear Regression  1398.459605            0.870226   \n",
       "1  Polynomial Regression (2nd)  1837.846259            0.915709   \n",
       "2             Ridge Regression  1359.385253            0.870010   \n",
       "3             Lasso Regression  1177.509135            0.927363   \n",
       "\n",
       "   R2_Score(test)  Cross-Validation  \n",
       "0        0.862187          0.848075  \n",
       "1        0.761983          0.848075  \n",
       "2        0.869781          0.778518  \n",
       "3        0.902295          0.742771  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict = pd.DataFrame(data = models, columns=['Model', 'RMSE', 'R2_Score(training)', 'R2_Score(test)', 'Cross-Validation'])\n",
    "predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型性能可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axe = plt.subplots(1,1, figsize=(18,6))\n",
    "\n",
    "predict.sort_values(by=['Cross-Validation'], ascending=False, inplace=True)\n",
    "\n",
    "sns.barplot(x='Cross-Validation', y='Model', data = predict, ax = axe)\n",
    "#axes[0].set(xlabel='Region', ylabel='Charges')\n",
    "axe.set_xlabel('Cross-Validaton Score', size=16)\n",
    "axe.set_ylabel('Model',size=16)\n",
    "axe.set_xlim(0,1.0)\n",
    "axe.set_xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, axes = plt.subplots(2, 1, figsize=(14, 10))\n",
    "\n",
    "predict.sort_values(by=['R2_Score(training)'], ascending=False, inplace=True)\n",
    "\n",
    "sns.barplot(x='R2_Score(training)',\n",
    "            y='Model',\n",
    "            data=predict,\n",
    "            palette='Blues_d',\n",
    "            ax=axes[0])\n",
    "#axes[0].set(xlabel='Region', ylabel='Charges')\n",
    "axes[0].set_xlabel('R2 Score (Training)', size=16)\n",
    "axes[0].set_ylabel('Model', size=16)\n",
    "axes[0].set_xlim(0, 1.0)\n",
    "axes[0].set_xticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "predict.sort_values(by=['R2_Score(test)'], ascending=False, inplace=True)\n",
    "\n",
    "sns.barplot(x='R2_Score(test)',\n",
    "            y='Model',\n",
    "            data=predict,\n",
    "            palette='Reds_d',\n",
    "            ax=axes[1])\n",
    "#axes[0].set(xlabel='Region', ylabel='Charges')\n",
    "axes[1].set_xlabel('R2 Score (Test)', size=16)\n",
    "axes[1].set_ylabel('Model', size=16)\n",
    "axes[1].set_xlim(0, 1.0)\n",
    "axes[1].set_xticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict.sort_values(by=['RMSE'], ascending=False, inplace=True)\n",
    "\n",
    "f, axe = plt.subplots(1, 1, figsize=(10, 6))\n",
    "sns.barplot(x='Model', y='RMSE', data=predict, ax=axe)\n",
    "axe.set_xlabel('Model', size=16)\n",
    "axe.set_ylabel('RMSE', size=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这段代码中，我使用丰田卡罗拉数据集构建了4个回归模型。这些是线性回归、多项式回归、岭回归、套索回归，然后衡量并可视化模型的性能。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
